{"id": "eadb479e6e25dd116aaec655854dd5589fe61c44554c151342aa1d36ea98854c", "sources": ["arxiv", "semantic_scholar"], "title": "Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data", "abstract": "Federated Learning (FL) enables collaborative and privacy-preserving model training across distributed clients, but most existing FL systems implicitly assume data stationarity. In real-world settings-such as healthcare, industrial IoT (IIOT), cybersecurity, and smart cities-data streams are inherently non-stationary, leading classical FL methods to suffer from performance degradation, instability, and catastrophic forgetting. Continual Learning (CL) addresses learning under evolving data distributions but has been largely studied in centralized settings, overlooking key constraints of federated systems, including privacy, limited communication, and client heterogeneity. Federated Continual Learning (FCL) emerges at the intersection of FL and CL, aiming to support lifelong, adaptive, and privacy-aware learning over distributed and non-stationary data. This survey provides a comprehensive and systematic overview of FCL. We first present a formal definition of the FCL problem and clarify its distinctive characteristics. We then analyze the limitations of classical FL under non-stationary conditions, highlighting how CL principles support long-term adaptation. To organize the rapidly growing literature, we propose a multi-dimensional taxonomy of FCL approaches. Furthermore, we review representative application domains and data modalities, summarize commonly used evaluation metrics, and discuss experimental perspectives for assessing long-term performance and forgetting. Finally, we highlight key open challenges, including handling extreme heterogeneity under temporal drift, designing scalable and privacy-preserving memory mechanisms, and establishing standardized benchmarks. This survey aims to serve as a reference and a roadmap for advancing FCL toward robust and deployable real-world systems.", "authors": ["Masoume Gholizade", "Fabrizio Ruffini", "Pietro Ducange", "Francesco Marcelloni"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-06-09", "url": "https://arxiv.org/abs/2606.11272", "pdf_url": "https://arxiv.org/pdf/2606.11272v1", "arxiv_id": "2606.11272", "doi": "10.1016/j.neucom.2026.133929", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Neurocomputing", "quality_score": 0.55}
{"id": "4d94643215c1f501a74253686f136f3c7fcb7e9ee53457ac92f513eeebb63f56", "sources": ["arxiv", "semantic_scholar"], "title": "Cognitive Threat Intelligence and Explainable Federated Security Analytics for distributed Infrastructure Systems", "abstract": "The increasing adoption of distributed infrastructure systems, cloud computing, Internet of Things (IoT) technologies, and edge-based architectures has significantly expanded the cybersecurity attack surface and introduced increasingly sophisticated cyber threats. Conventional centralized intrusion detection approaches often face challenges related to scalability, data privacy, communication overhead, and limited transparency in artificial intelligence-driven decision-making processes. To address these limitations, this study proposes a Cognitive Threat Intelligence and Explainable Federated Security Analytics framework for distributed infrastructure systems. The proposed framework integrates Federated Learning (FL), Explainable Artificial Intelligence (XAI), and cognitive cybersecurity analytics to enable collaborative and privacy-preserving cyber threat detection across distributed network environments. Instead of transmitting sensitive raw network traffic data to centralized servers, local security models are independently trained at distributed nodes, where only encrypted model parameters and updates are shared through a federated aggregation mechanism. This decentralized learning architecture improves privacy protection while reducing communication dependency and centralized security risks. To enhance intelligent threat analysis, the framework incorporates machine learning and deep learning algorithms including Random Forest, XGBoost, Autoencoder", "authors": ["Md. Arifur Rahman", "B. M. Taslimul Haque", "Md. Iqbal Hossan", "Md. Serajul Kabir Chowdhury Rubel"], "categories": ["cs.CR", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-06-04", "url": "https://arxiv.org/abs/2606.05701", "pdf_url": "https://arxiv.org/pdf/2606.05701v1", "arxiv_id": "2606.05701", "doi": "10.64882/ijrt.v13.i1.1384", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Journal of Research and Technology (IJRT), Volume 13, Issue 01, January-March 2025, pp. 132-151", "quality_score": 0.55}
{"id": "76b0d70e6afd3c627a2b97ad5658e2614944870e2b64d193f8ed5a03721ec5d6", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning for Multi-Center Sepsis Early Prediction with Privacy-Preserving", "abstract": "Privacy-sensitive and distributed characteristics of multi-center medical data bring severe obstacles to centralized modeling for accurate early prediction of sepsis. Federated learning (FL) has attracted growing attention as a promising framework for collaborative model development, as it allows multiple institutions to jointly train predictive models without directly sharing or centralizing raw data. Nevertheless, its practical performance, robustness, and privacy-preserving benefits remain insufficiently evaluated using real-world clinical datasets. To bridge this gap, this study systematically examines the application of federated learning to multi-center sepsis prediction. The experimental dataset consists of 648 clinically screened samples collected from three tertiary hospitals in China, with rigorous inclusion and exclusion criteria. We establish a centralized training paradigm as the performance baseline, and then implement a horizontal federated learning framework for distributed collaborative modeling. Extensive experimental results demonstrate that the federated learning-based model achieves highly comparable prediction accuracy to the centralized counterpart, while fundamentally avoiding privacy leakage. Further privacy security analysis verifies that malicious attackers cannot reconstruct the original patient data from the transmitted model parameters, indicating strong resistance against data reconstruction attacks. This work not only validates the practicality and security of federated learning in clinical sepsis prediction, but also provides a reliable and feasible solution for privacy-preserving multi-center medical collaboration.", "authors": ["Xixi Tian", "Di Wu", "Xiang Liu", "Yiziting Zhu", "Yujie Li", "Xin Shu", "Bin Yi"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2026-06-03", "url": "https://arxiv.org/abs/2606.04338", "pdf_url": "https://arxiv.org/pdf/2606.04338v1", "arxiv_id": "2606.04338", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "acbe063f3ed25f024563a10dca445939648e61fb957134c2f212dbc638e7f6e9", "sources": ["arxiv", "semantic_scholar"], "title": "IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning", "abstract": "Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\\varepsilon_i$) according to institutional policies and data sensitivity. In practice, many HDP-FL systems employ $\\varepsilon$-aware server aggregation to improve model utility by re-weighting client updates according to their declared privacy budgets. However, gradient updates in FL retain structural patterns induced by non-independent and identically-distributed (non-IID) data, and these additional signals exposed by $\\varepsilon$-aware aggregation create new opportunities for inference by an honest-but-curious server. In this work, we first show that a server equipped with gradient denoising and surrogate modeling can mount a \\emph{Privacy Inference Attack} that infers distributional attributes of clients and links updates from the same client across training rounds, measured via surrogate inference accuracy and linkage success, under realistic knowledge constraints. The Shuffle-Model has been widely studied as a defense against such inference risks by anonymizing update sources, but it is fundamentally incompatible with HDP-FL $\\varepsilon$-aware aggregation. To address this challenge, we propose \\textbf{IntraShuffler}, a middleware defense framework designed for HDP-FL systems. IntraShuffler introduces a privacy-aware shuffling mechanism that groups clients into privacy-compatible buckets and performs parameter-level shuffling within each bucket to disrupt persistent gradient structure while preserving $\\varepsilon$-aware aggregation. Experiments across four different datasets show that IntraShuffler reduces gradient recoverability by over 60% and decreases surrogate inference accuracy from 0.78 to 0.33 while maintaining comparable model utility across multiple FL aggregation rules.", "authors": ["Farhin Farhad Riya", "Olivera Kotevska", "Jinyuan Stella Sun"], "categories": ["cs.LG", "cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2026-06-01", "url": "https://arxiv.org/abs/2606.02563", "pdf_url": "https://arxiv.org/pdf/2606.02563v1", "arxiv_id": "2606.02563", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "5a9195622d010f321424727e4a3f35f5f3ea56d523f28eaf156af74f68235a0d", "sources": ["arxiv", "semantic_scholar"], "title": "GuidaPA: Privacy-Preserving Chatbot for Public Administration via Federated Learning", "abstract": "We present GuidaPA, a privacy-preserving chatbot for the Italian Public Administration (PA) trained via Federated Learning (FL) on documentation from two national PA platforms, SIGESON and SIDFORS. Our corpus includes approximately 8 pages of SIGESON manuals and 31 pages of SIDFORS manuals/FAQs; while this study uses public documentation as a safe proxy, the intended deployment extends to restricted internal sources (e.g., tickets, officer manuals, database extracts) that can not be centrally pooled due to regulatory and organizational constraints. GuidaPA integrates role-based access control, secure client-side preprocessing, explicit monitoring of non-IID effects, and parameter-efficient federated fine-tuning of large language models. Using QLoRA (4-bit) over 15 federated rounds with an 80/20 train-test split per client, we evaluate answer quality with ROUGE, BLEU-4, and METEOR. The best federated model achieves ROUGE-1/2/L of 61.10/55.77/59.44, BLEU-4 of 45.02, and METEOR of 63.94-close to private centralized fine-tuning while keeping data on-site. Compared to the general-purpose baseline, domain fine-tuning improves ROUGE-1 from 41.45 to 62.18 and BLEU-4 from 26.97 to 50.90. Overall, the results indicate that FL can deliver high-quality conversational AI for public services without centralized data sharing", "authors": ["Daniel M. Jimenez-Gutierrez", "Albenzio Cirillo", "Raffaele Nicolussi", "Alessio Beltrame", "Andrea Vitaletti"], "categories": ["cs.AI", "cs.CL", "cs.DC", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-31", "url": "https://arxiv.org/abs/2606.01386", "pdf_url": "https://arxiv.org/pdf/2606.01386v1", "arxiv_id": "2606.01386", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "66fcb2d75b922ef1cb67afe5eb0e122e72ea601726caed06b0892380e073fac9", "sources": ["arxiv", "semantic_scholar"], "title": "Pattern Recognition Tasks with Personalized Federated Learning", "abstract": "Personalized Federated Learning (PFL) constitutes a novel paradigm that tailors Machine Learning (ML) models to individual clients, thereby furnishing personalized model updates whilst upholding stringent data privacy principles. Diverging from conventional standard Federated Learning (FL) approaches, PFL adapts models to distinct client data distributions, engendering heightened levels of accuracy, customization, and data security, all while minimizing communication overhead. This methodology proves particularly salient in contexts marked by pattern recognition tasks reliant upon heterogeneous data sources and underpinned by paramount privacy apprehensions. In the present research endeavor, this article undertake a comprehensive comparative analysis of seven distinct PFL algorithms deployed across three diverse datasets, namely MNIST, SignMNIST, and Digit5. The overarching objective entails ascertaining the preeminent PFL algorithm, within the framework of pattern recognition tasks, through a rigorous evaluation anchored in metrics encompassing Accuracy, Precision, Recall, and F1 Score. Concurrently, an in-depth scrutiny of these PFL algorithms is conducted, elucidating their operative workflows, advantages, and limitations. Through empirical investigation, the findings evince that APPLE, FedGC, and FedProto emerge as stalwart contenders, consistently furnishing superior performance across the spectrum of assessed datasets, while acknowledging the contextual specificity of alternative algorithms and the potential for iterative refinement to realize optimal outcomes.", "authors": ["Md. Arifur Rahman", "Isha Das", "Mushfiqur Rahman Abir", "B. M. Taslimul Haque", "Abdullah Al Noman", "Abir Ahmed", "Md. Jakir Hossen"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-27", "url": "https://arxiv.org/abs/2605.27816", "pdf_url": "https://arxiv.org/pdf/2605.27816v1", "arxiv_id": "2605.27816", "doi": "10.28991/ESJ-2026-010-02-020", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Emerging Science Journal", "quality_score": 0.55}
{"id": "64c085ae846117e9ea3c474fb3e7cc32dec30934c132997e8d38b234e6b7b133", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning by Utility-Constrained Stochastic Aggregation for Improving Rational Participation", "abstract": "Federated Learning (FL) algorithms implicitly assume that clients passively comply with server-side orchestration by sharing local model updates upon server request. However, this overlooks an important aspect in real-world cross-silo environments: clients are often rational agents who may prioritize their utilities such as local model performance over that of the global model. In settings with significant statistical heterogeneity, rational clients may opt out of the federation if the perceived benefits of collaboration fail to meet their local utility thresholds. Such attrition degrades the global model performance and can lead to the collapse of the federated training process. In this work, we introduce FedUCA, (Federated Learning by Utility-Constrained Stochastic Aggregation for Improving Rational Participation), a framework that formalizes the server's role as an optimizer seeking to maximize global model performance by sustaining client participation. We substantiate our framework through extensive experiments on standard datasets demonstrating that by prioritizing participation feasibility, FedUCA achieves significantly higher client retention and, consequently, a superior global model performance.", "authors": ["M Yashwanth", "Arunabh Singh", "Ashok Nayak", "Sai Kiran Bulusu", "Anirban Chakraborty"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-18", "url": "https://arxiv.org/abs/2605.18020", "pdf_url": "https://arxiv.org/pdf/2605.18020v1", "arxiv_id": "2605.18020", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "922cdbc9ebea3719fbf1cbdc23b9aac5ba2babaf52ad2a382613366131d2acac", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Generation Fraud Detection for Distributed Photovoltaic Systems: A Solar Irradiance-Fused Federated Learning Framework", "abstract": "The wide adoption of residential photovoltaic (PV) systems introduces new challenges for generation fraud detection (FD). Unlike traditional electricity theft detection, which focuses on electricity consumption-side behavior, PV generation fraud detection (PVG-FD) is complicated by the inherent intermittency and uncertainty of PV generation. The distributed nature of PV systems poses further challenges for centralized PVG-FD approaches due to scalability and privacy concerns. This paper develops a privacy-preserving distributed PVG-FD framework based on federated learning (FL). In this framework, a utility company manages multiple household communities, where each of which is equipped with a local detector. The framework integrates a novel detection model architecture with privacy-preserving global collaboration. Each community's local model fuses PV generation and weather data via a co-attention mechanism to detect discrepancies critical for PVG-FD. The FL framework enables cross-community collaboration by aggregating model parameters and prototypes, leveraging global knowledge sharing with local refinement while preserving privacy. It also uses prototype alignment to address class imbalance by enhancing fraud sample representation. Extensive experiments on a real-world residential PV dataset validate the effectiveness of the developed method and demonstrate that it outperforms state-of-the-art FL methods across various scenarios. The results also show its scalability across varying community sizes and strong robustness to class imbalance.", "authors": ["Xiaolu Chen", "Chenghao Huang", "Yanru Zhang", "Hao Wang"], "categories": ["cs.LG", "cs.CE"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-16", "url": "https://arxiv.org/abs/2605.17039", "pdf_url": "https://arxiv.org/pdf/2605.17039v1", "arxiv_id": "2605.17039", "doi": "10.1109/TSG.2026.3692585", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Smart Grid", "quality_score": 0.55}
{"id": "f5a0efd59bdfda557ddc1a95cb5cc60fc05f6e0ed6d60196ec35bea8e1a9dd28", "sources": ["arxiv", "semantic_scholar"], "title": "Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning", "abstract": "Hierarchical federated learning (HFL) leverages edge servers for partial aggregation in edge computing. Yet existing FL methods lack mechanisms for jointly optimizing cluster assignment and client selection under data heterogeneity. This paper proposes Fed-BAC, which integrates additive cluster personalization with a two-level bandit framework: contextual bandits at the cloud learn server-to-cluster assignments, while Thompson Sampling at each edge server identifies high-contributing clients. The additive decomposition enables the sharing of knowledge between groups through a globally aggregated network, while cluster-specific networks capture distribution variations. Across three classification benchmarks (CIFAR-10, SVHN, Fashion-MNIST) under moderate ($α= 0.5$) and severe ($α= 0.1$) Dirichlet non-IID partitioning, Fed-BAC achieves distributed accuracy gains of up to +35.5pp over HierFAVG and +8.4pp over IFCA, while requiring only 80% client participation, converging 1.5 to 4.8$\\times$ faster depending on dataset and accuracy target, and improving cross-server fairness. These gains are further validated at 5$\\times$ deployment scale on CIFAR-10. The advantage of Fed-BAC increases with heterogeneity severity, confirming that additive cluster personalization becomes increasingly valuable as data distributions diverge.", "authors": ["Satwat Bashir", "Tasos Dagiuklas", "Muddesar Iqbal"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-12", "url": "https://arxiv.org/abs/2605.11815", "pdf_url": "https://arxiv.org/pdf/2605.11815v1", "arxiv_id": "2605.11815", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "e6c402ecb8ca68e36913e884ebe392e1001bb5b9cb5ae343ad3d9bc88d050d45", "sources": ["arxiv", "semantic_scholar"], "title": "FLAM: Evaluating Model Performance with Aggregatable Measures in Federated Learning", "abstract": "Performance evaluation is essential for assessing the quality of machine learning (ML) models and guiding deployment decisions. In federated learning (FL), assessing the performance is challenging because data are distributed across participants. Consequently, the coordinator must rely on locally computed evaluation metrics and aggregate them to assess the global model. A key challenge is that common aggregation strategies, such as weighted averaging based on the local samples per participant, do not always produce the same results as centralized evaluation. Existing definitions of performance evaluation are largely tailored to accuracy and do not generalize to other metrics, leading to inconsistencies between participant-based and centralized evaluation. However, such discrepancies are inconsistent with the FL objective and lead to a wrong calculation of the metric. To address this issue, we examine the underlying reasons for these discrepancies and propose FLAM, a performance evaluation method based on aggregatable measures that yields the same results as centralized evaluation without the need for a global test dataset.", "authors": ["Fabian Stricker", "Jose A. Peregrina", "David Bermbach", "Christian Zirpins"], "categories": ["cs.LG", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-08", "url": "https://arxiv.org/abs/2605.07962", "pdf_url": "https://arxiv.org/pdf/2605.07962v1", "arxiv_id": "2605.07962", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "19c1ff65552994f383a91d57e6607c6e543bd38cb135c493bf222887e2bcfce2", "sources": ["arxiv", "semantic_scholar"], "title": "UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment", "abstract": "Device-free localization trains models from heterogeneous wireless and visual sensors (e.g., Wi-Fi, LiDAR) distributed across edge devices. Federated learning offers a privacy-respecting framework, but is brittle when clients differ in sensor modality and resolution, when their data distributions drift, and when privacy noise destroys the structural signal needed for localization. We propose UMEDA, a graph federated learning framework in which clients form nodes of a global graph that share a continuous integral operator, and aggregation is reformulated as spectral signal processing on this operator. Each client encodes its local sensors with a linear-attention layer whose kernel spectrum is low-rank filtered, suppressing modality-specific residuals so clients with different sensors align in a common low-rank subspace. The server then aggregates client updates via a diffusion model over the kernel's spectral coefficients, treating updates as discretizations of a shared operator rather than topology-bound weights -- this absorbs varying graph sizes and missing modalities without node-wise correspondence. To balance privacy and utility, we add an anisotropic differential-privacy mechanism that projects noise preferentially into the null space of the signal subspace, preserving dominant eigendirections while ensuring formal $(ε, δ)$-DP under gradient clipping. On MM-Fi and the RELI11D out-of-distribution benchmark, UMEDA outperforms state-of-the-art federated baselines in accuracy, convergence, and communication efficiency, particularly under high modality heterogeneity and tight privacy budgets.", "authors": ["Shih-Yu Lai", "Hirozumi Yamaguchi", "Shang-Tse Chen", "Yu-Lun Liu", "Bing-Yu Chen"], "categories": ["cs.LG", "cs.AI", "cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-08", "url": "https://arxiv.org/abs/2605.08288", "pdf_url": "https://arxiv.org/pdf/2605.08288v1", "arxiv_id": "2605.08288", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "0d5ac953659b5d5b8b200ddbe1dd5c92a3b8b52561a5d02f3c420d78d99d7897", "sources": ["arxiv", "semantic_scholar"], "title": "FL-Sailer: Efficient and Privacy-Preserving Federated Learning for Scalable Single-Cell Epigenetic Data Analysis via Adaptive Sampling", "abstract": "Single-cell ATAC-seq (scATAC-seq) enables high-resolution mapping of chromatin accessibility, yet privacy regulations and data size constraints hinder multi-institutional sharing. Federated learning (FL) offers a privacy-preserving alternative, but faces three fundamental barriers in scATAC-seq analysis: ultra-high dimensionality, extreme sparsity, and severe cross-institutional heterogeneity. We propose FL-Sailer, the first FL framework designed for scATAC-seq data. FL-Sailer integrates two key innovations: (i) adaptive leverage score sampling, which selects biologically interpretable features while reducing dimensionality by 80%, and (ii) an invariant VAE architecture, which disentangles biological signals from technical confounders via mutual information minimization. We provide a convergence guarantee, showing that FL-Sailer converges to an approximate solution of the original high-dimensional problem with bounded error. Extensive experiments on synthetic and real epigenomic datasets demonstrate that FL-Sailer not only enables previously infeasible multi-institutional collaborations but also surpasses centralized methods by leveraging adaptive sampling as an implicit regularizer to suppress technical noise. Our work establishes that federated learning, when tailored to domain-specific challenges, can become a superior paradigm for collaborative epigenomic research.", "authors": ["Guangyi Zhang", "Yi Dai", "Yiyun He", "Junhao Liu"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2026-05-06", "url": "https://arxiv.org/abs/2605.04519", "pdf_url": "https://arxiv.org/pdf/2605.04519v1", "arxiv_id": "2605.04519", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Transactions on Machine Learning Research (TMLR), May 2026", "quality_score": 0.55}
{"id": "6fc9bf036962a0236387dd4e9952fd0513753951b8288cd825cda3f0d9c69a28", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning", "abstract": "The growing development of artificial intelligence based solutions, together with privacy legislation, has driven the rise of the so-called privacy preserving machine learning architectures, such as federated learning. While federated learning enables model training on decentralized data preventing their sharing and centralization, it still faces several challenges related to data integrity and privacy. This paper presents a comprehensive privacy preserving federated learning workflow for sensitive tabular data, including anonymization and differential privacy techniques. We also introduce a formal definition for the concept of client drift, together with ways of detecting it to mitigate poisoning attacks. Then, we detail a complete methodology for assigning personalized privacy budgets for global differential privacy to the different clients participating in the network, based on a re-identification risk metric. The proposed methodology is presented and tested on an openly available dataset of medical records. Within the experimental setup we show that the approach based on personalized budgets, compared to the architecture including global differential privacy with fixed privacy budget, achieves a better model performance in terms of two error metrics.", "authors": ["Judith Sáinz-Pardo Díaz", "Álvaro López García"], "categories": ["cs.CR", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-04", "url": "https://arxiv.org/abs/2605.02372", "pdf_url": "https://arxiv.org/pdf/2605.02372v1", "arxiv_id": "2605.02372", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "562b0c01a588450f2dd34c21a7916337bb3f578e4f39c70545183bbafd342519", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Federated Learning Framework for Distributed Chemical Process Optimization", "abstract": "Industrial chemical plants often operate under strict data confidentiality constraints, making centralized data-driven process modeling difficult. Federated learning (FL) provides a promising solution by enabling collaborative model training across distributed facilities without sharing raw operational data. This paper proposes a privacy-preserving federated learning framework for distributed chemical process optimization using data collected from multiple geographically separated plants. Each plant locally trains a neural-network-based process model using its own time-series sensor data, while only model parameters are transmitted to a central aggregation server through secure aggregation mechanisms. This design allows cross-plant knowledge sharing while maintaining strict data locality and industrial confidentiality. Experimental evaluation was conducted using process datasets from three independent chemical plants operating under heterogeneous conditions. The results demonstrate rapid convergence of the federated model, with the global mean squared error decreasing from approximately 2369 to below 50 within the first five communication rounds and stabilizing around 35 after 40 rounds. In comparison with local-only training, the proposed federated framework significantly improves prediction accuracy across all plants, while achieving performance comparable to centralized training. The findings indicate that federated learning provides an effective and scalable solution for collaborative industrial analytics, enabling privacy-preserving predictive modeling and process optimization across distributed chemical production facilities.", "authors": ["Teetat Pipattaratonchai", "Aueaphum Aueawatthanaphisut"], "categories": ["cs.LG", "cs.AI", "eess.SY"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2026-04-28", "url": "https://arxiv.org/abs/2604.26073", "pdf_url": "https://arxiv.org/pdf/2604.26073v1", "arxiv_id": "2604.26073", "doi": "10.48550/arXiv.2604.26073", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.55}
{"id": "6b7ad21107b2d70f7075053d9c0a0027b77471d6aab2d3ef19f411c67aa27fe0", "sources": ["arxiv", "semantic_scholar"], "title": "FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels", "abstract": "Federated learning (FL) enables collaborative model training without sharing raw data; however, the presence of noisy labels across distributed clients can severely degrade the learning performance. In this paper, we propose FedSIR, a multi-stage framework for robust FL under noisy labels. Different from existing approaches that mainly rely on designing noise-tolerant loss functions or exploiting loss dynamics during training, our method leverages the spectral structure of client feature representations to identify and mitigate label noise. Our framework consists of three key components. First, we identify clean and noisy clients by analyzing the spectral consistency of class-wise feature subspaces with minimal communication overhead. Second, clean clients provide spectral references that enable noisy clients to relabel potentially corrupted samples using both dominant class directions and residual subspaces. Third, we employ a noise-aware training strategy that integrates logit-adjusted loss, knowledge distillation, and distance-aware aggregation to further stabilize federated optimization. Extensive experiments on standard FL benchmarks demonstrate that FedSIR consistently outperforms state-of-the-art methods for FL with noisy labels. The code is available at https://github.com/sinagh72/FedSIR.", "authors": ["Sina Gholami", "Abdulmoneam Ali", "Tania Haghighi", "Ahmed Arafa", "Minhaj Nur Alam"], "categories": ["cs.LG", "cs.AI", "cs.CV", "cs.DC", "eess.SP"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2026-04-22", "url": "https://arxiv.org/abs/2604.20825", "pdf_url": "https://arxiv.org/pdf/2604.20825v1", "arxiv_id": "2604.20825", "doi": "10.48550/arXiv.2604.20825", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/sinagh72/FedSIR", "venue": "arXiv.org", "quality_score": 0.85}
{"id": "54814e9e7ffefba53dcf1b8aec2eb1559d4be334bc21033f65b15ed46456965e", "sources": ["arxiv", "semantic_scholar"], "title": "Secure and Privacy-Preserving Vertical Federated Learning", "abstract": "We propose a novel end-to-end privacy-preserving framework, instantiated by three efficient protocols for different deployment scenarios, covering both input and output privacy, for the vertically split scenario in federated learning (FL), where features are split across clients and labels are not shared by all parties. We do so by distributing the role of the aggregator in FL into multiple servers and having them run secure multiparty computation (MPC) protocols to perform model and feature aggregation and apply differential privacy (DP) to the final released model. While a naive solution would have the clients delegating the entirety of training to run in MPC between the servers, our optimized solution, which supports purely global and also global-local models updates with privacy-preserving, drastically reduces the amount of computation and communication performed using multiparty computation. The experimental results also show the effectiveness of our protocols.", "authors": ["Shan Jin", "Sai Rahul Rachuri", "Yizhen Wang", "Anderson C. A. Nascimento", "Yiwei Cai"], "categories": ["cs.CR", "cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-15", "url": "https://arxiv.org/abs/2604.13474", "pdf_url": "https://arxiv.org/pdf/2604.13474v1", "arxiv_id": "2604.13474", "doi": "10.48550/arXiv.2604.13474", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5443}
{"id": "8192a4860026aa41026710ae4865f67246dd1bf932dbb21c2ca518d346d929e0", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning for Privacy-Preserving Medical AI", "abstract": "This dissertation investigates privacy-preserving federated learning for Alzheimer's disease classification using three-dimensional MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Existing methodologies often suffer from unrealistic data partitioning, inadequate privacy guarantees, and insufficient benchmarking, limiting their practical deployment in healthcare. To address these gaps, this research proposes a novel site-aware data partitioning strategy that preserves institutional boundaries, reflecting real-world multi-institutional collaborations and data heterogeneity. Furthermore, an Adaptive Local Differential Privacy (ALDP) mechanism is introduced, dynamically adjusting privacy parameters based on training progression and parameter characteristics, thereby significantly improving the privacy-utility trade-off over traditional fixed-noise approaches. Systematic empirical evaluation across multiple client federations and privacy budgets demonstrated that advanced federated optimisation algorithms, particularly FedProx, could equal or surpass centralised training performance while ensuring rigorous privacy protection. Notably, ALDP achieved up to 80.4% accuracy in a two-client configuration, surpassing fixed-noise Local DP by 5-7 percentage points and demonstrating substantially greater training stability. The comprehensive ablation studies and benchmarking establish quantitative standards for privacy-preserving collaborative medical AI, providing practical guidelines for real-world deployment. This work thereby advances the state-of-the-art in federated learning for medical imaging, establishing both methodological foundations and empirical evidence necessary for future privacy-compliant AI adoption in healthcare.", "authors": ["Tin Hoang"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-16", "url": "https://arxiv.org/abs/2603.15901", "pdf_url": "https://arxiv.org/pdf/2603.15901v1", "arxiv_id": "2603.15901", "doi": "10.48550/arXiv.2603.15901", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5099}
{"id": "40cdc708566be4c1db37bd0f1b85ec1dc7e896f888fb8445483f4a753a51a409", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Few-Shot Learning on Neuromorphic Hardware: An Empirical Study Across Physical Edge Nodes", "abstract": "Federated learning on neuromorphic hardware remains unexplored because on-chip spike-timing-dependent plasticity (STDP) produces binary weight updates rather than the floating-point gradients assumed by standard algorithms. We build a two-node federated system with BrainChip Akida AKD1000 processors and run approximately 1,580 experimental trials across seven analysis phases. Of four weight-exchange strategies tested, neuron-level concatenation (FedUnion) consistently preserves accuracy while element-wise weight averaging (FedAvg) destroys it (p = 0.002). Domain-adaptive fine-tuning of the upstream feature extractor accounts for most of the accuracy gains, confirming feature quality as the dominant factor. Scaling feature dimensionality from 64 to 256 yields 77.0% best-strategy federated accuracy (n=30, p < 0.001). Two independent asymmetries (wider features help federation more than individual learning, while binarization hurts federation more) point to a shared prototype complementarity mechanism: cross-node transfer scales with the distinctiveness of neuron prototypes.", "authors": ["Steven Motta", "Gioele Nanni"], "categories": ["cs.NE", "cs.DC", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-13", "url": "https://arxiv.org/abs/2603.13037", "pdf_url": "https://arxiv.org/pdf/2603.13037v1", "arxiv_id": "2603.13037", "doi": "10.48550/arXiv.2603.13037", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Stemo688/federated-neuromorphic-learning", "venue": "arXiv.org", "quality_score": 0.7827}
{"id": "3d463da2ab49bb6136443e18c4b362c94604c1c87d6780e5747a7127c84fc202", "sources": ["arxiv", "semantic_scholar"], "title": "Understanding the Resource Cost of Fully Homomorphic Encryption in Quantum Federated Learning", "abstract": "Quantum Federated Learning (QFL) enables distributed training of Quantum Machine Learning (QML) models by sharing model gradients instead of raw data. However, these gradients can still expose sensitive user information. To enhance privacy, homomorphic encryption of parameters has been proposed as a solution in QFL and related frameworks. In this work, we evaluate the overhead introduced by Fully Homomorphic Encryption (FHE) in QFL setups and assess its feasibility for real-world applications. We implemented various QML models including a Quantum Convolutional Neural Network (QCNN) trained in a federated environment with parameters encrypted using the CKKS scheme. This work marks the first QCNN trained in a federated setting with CKKS-encrypted parameters. Models of varying architectures were trained to predict brain tumors from MRI scans. The experiments reveal that memory and communication overhead remain substantial, making FHE challenging to deploy. Minimizing overhead requires reducing the number of model parameters, which, however, leads to a decline in classification performance, introducing a trade-off between privacy and model complexity.", "authors": ["Lukas Böhm", "Arjhun Swaminathan", "Anika Hannemann", "Erik Buchmann"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-03", "url": "https://arxiv.org/abs/2603.02799", "pdf_url": "https://arxiv.org/pdf/2603.02799v1", "arxiv_id": "2603.02799", "doi": "10.48550/arXiv.2603.02799", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.495}
{"id": "751ba69b2b94bf9a9df91a39068a2f88dd9d057d64739ae6ced6f340400e9af1", "sources": ["arxiv", "semantic_scholar"], "title": "SRFed: Mitigating Poisoning Attacks in Privacy-Preserving Federated Learning with Heterogeneous Data", "abstract": "Federated Learning (FL) enables collaborative model training without exposing clients' private data, and has been widely adopted in privacy-sensitive scenarios. However, FL faces two critical security threats: curious servers that may launch inference attacks to reconstruct clients' private data, and compromised clients that can launch poisoning attacks to disrupt model aggregation. Existing solutions mitigate these attacks by combining mainstream privacy-preserving techniques with defensive aggregation strategies. However, they either incur high computation and communication overhead or perform poorly under non-independent and identically distributed (Non-IID) data settings. To tackle these challenges, we propose SRFed, an efficient Byzantine-robust and privacy-preserving FL framework for Non-IID scenarios. First, we design a decentralized efficient functional encryption (DEFE) scheme to support efficient model encryption and non-interactive decryption. DEFE also eliminates third-party reliance and defends against server-side inference attacks. Second, we develop a privacy-preserving defensive model aggregation mechanism based on DEFE. This mechanism filters poisonous models under Non-IID data by layer-wise projection and clustering-based analysis. Theoretical analysis and extensive experiments show that SRFed outperforms state-of-the-art baselines in privacy protection, Byzantine robustness, and efficiency.", "authors": ["Yiwen Lu"], "categories": ["cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-18", "url": "https://arxiv.org/abs/2602.16480", "pdf_url": "https://arxiv.org/pdf/2602.16480v1", "arxiv_id": "2602.16480", "doi": "10.48550/arXiv.2602.16480", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4801}
{"id": "e0e74e8a42015e3f3d1d36ad2d44e87219ce8c498e70698f7dc720e50f41266e", "sources": ["arxiv", "semantic_scholar"], "title": "Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimization", "abstract": "Collaborative clinical decision support is often constrained by governance and privacy rules that prevent pooling patient-level records across institutions. We present a hybrid privacy-preserving framework that combines Federated Learning (FL) and Split Learning (SL) to support decision-oriented healthcare modeling without raw-data sharing. The approach keeps feature-extraction trunks on clients while hosting prediction heads on a coordinating server, enabling shared representation learning and exposing an explicit collaboration boundary where privacy controls can be applied. Rather than assuming distributed training is inherently private, we audit leakage empirically using membership inference on cut-layer representations and study lightweight defenses based on activation clipping and additive Gaussian noise. We evaluate across three public clinical datasets under non-IID client partitions using a unified pipeline and assess performance jointly along four deployment-relevant axes: factual predictive utility, uplift-based ranking under capacity constraints, audited privacy leakage, and communication overhead. Results show that hybrid FL-SL variants achieve competitive predictive performance and decision-facing prioritization behavior relative to standalone FL or SL, while providing a tunable privacy-utility trade-off that can reduce audited leakage without requiring raw-data sharing. Overall, the work positions hybrid FL-SL as a practical design space for privacy-preserving healthcare decision support where utility, leakage risk, and deployment cost must be balanced explicitly.", "authors": ["Farzana Akter", "Rakib Hossain", "Deb Kanna Roy Toushi", "Mahmood Menon Khan", "Sultana Amin", "Lisan Al Amin"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-17", "url": "https://arxiv.org/abs/2602.15304", "pdf_url": "https://arxiv.org/pdf/2602.15304v1", "arxiv_id": "2602.15304", "doi": "10.1109/SoutheastCon63549.2026.11476559", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "SoutheastCon", "quality_score": 0.479}
{"id": "ffd4c7f8a6af305c960bcd8fb055bb357281c2546865e7a3e06c5fe258f73f4e", "sources": ["arxiv", "semantic_scholar"], "title": "DeepFusion: Accelerating MoE Training via Federated Knowledge Distillation from Heterogeneous Edge Devices", "abstract": "Recent Mixture-of-Experts (MoE)-based large language models (LLMs) such as Qwen-MoE and DeepSeek-MoE are transforming generative AI in natural language processing. However, these models require vast and diverse training data. Federated learning (FL) addresses this challenge by leveraging private data from heterogeneous edge devices for privacy-preserving MoE training. Nonetheless, traditional FL approaches require devices to host local MoE models, which is impractical for resource-constrained devices due to large model sizes. To address this, we propose DeepFusion, the first scalable federated MoE training framework that enables the fusion of heterogeneous on-device LLM knowledge via federated knowledge distillation, yielding a knowledge-abundant global MoE model. Specifically, DeepFusion features each device to independently configure and train an on-device LLM tailored to its own needs and hardware limitations. Furthermore, we propose a novel View-Aligned Attention (VAA) module that integrates multi-stage feature representations from the global MoE model to construct a predictive perspective aligned with on-device LLMs, thereby enabling effective cross-architecture knowledge distillation. By explicitly aligning predictive perspectives, VAA resolves the view-mismatch problem in traditional federated knowledge distillation, which arises from heterogeneity in model architectures and prediction behaviors between on-device LLMs and the global MoE model. Experiments with industry-level MoE models (Qwen-MoE and DeepSeek-MoE) and real-world datasets (medical and finance) demonstrate that DeepFusion achieves performance close to centralized MoE training. Compared with key federated MoE baselines, DeepFusion reduces communication costs by up to 71% and improves token perplexity by up to 5.28%.", "authors": ["Songyuan Li", "Jia Hu", "Ahmed M. Abdelmoniem", "Geyong Min", "Haojun Huang", "Jiwei Huang"], "categories": ["cs.LG", "cs.AI", "cs.MA"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-15", "url": "https://arxiv.org/abs/2602.14301", "pdf_url": "https://arxiv.org/pdf/2602.14301v1", "arxiv_id": "2602.14301", "doi": "10.48550/arXiv.2602.14301", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4767}
{"id": "1a5ccc4a933861100fca2180280d485288ee24081ac0903dba61f0d851332fdd", "sources": ["arxiv", "semantic_scholar"], "title": "Safeguarding Privacy: Privacy-Preserving Detection of Mind Wandering and Disengagement Using Federated Learning in Online Education", "abstract": "Since the COVID-19 pandemic, online courses have expanded access to education, yet the absence of direct instructor support challenges learners' ability to self-regulate attention and engagement. Mind wandering and disengagement can be detrimental to learning outcomes, making their automated detection via video-based indicators a promising approach for real-time learner support. However, machine learning-based approaches often require sharing sensitive data, raising privacy concerns. Federated learning offers a privacy-preserving alternative by enabling decentralized model training while also distributing computational load. We propose a framework exploiting cross-device federated learning to address different manifestations of behavioral and cognitive disengagement during remote learning, specifically behavioral disengagement, mind wandering, and boredom. We fit video-based cognitive disengagement detection models using facial expressions and gaze features. By adopting federated learning, we safeguard users' data privacy through privacy-by-design and introduce a novel solution with the potential for real-time learner support. We further address challenges posed by eyeglasses by incorporating related features, enhancing overall model performance. To validate the performance of our approach, we conduct extensive experiments on five datasets and benchmark multiple federated learning algorithms. Our results show great promise for privacy-preserving educational technologies promoting learner engagement.", "authors": ["Anna Bodonhelyi", "Mengdi Wang", "Efe Bozkir", "Babette Bühler", "Enkelejda Kasneci"], "categories": ["cs.LG", "cs.HC"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-10", "url": "https://arxiv.org/abs/2602.09904", "pdf_url": "https://arxiv.org/pdf/2602.09904v1", "arxiv_id": "2602.09904", "doi": "10.48550/arXiv.2602.09904", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4709}
{"id": "6beb5eb16c96091446c9a91001bde7a800deadb7276ea79ad321607196278f00", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Federated Learning with Verifiable Fairness Guarantees", "abstract": "Federated learning enables collaborative model training across distributed institutions without centralizing sensitive data; however, ensuring algorithmic fairness across heterogeneous data distributions while preserving privacy remains fundamentally unresolved. This paper introduces CryptoFair-FL, a novel cryptographic framework providing the first verifiable fairness guarantees for federated learning systems under formal security definitions. The proposed approach combines additively homomorphic encryption with secure multi-party computation to enable privacy-preserving verification of demographic parity and equalized odds metrics without revealing protected attribute distributions or individual predictions. A novel batched verification protocol reduces computational complexity from BigO(n^2) to BigO(n \\log n) while maintaining (\\dparam, \\deltap)-differential privacy with dparam = 0.5 and deltap = 10^{-6}. Theoretical analysis establishes information-theoretic lower bounds on the privacy cost of fairness verification, demonstrating that the proposed protocol achieves near-optimal privacy-fairness tradeoffs. Comprehensive experiments across four benchmark datasets (MIMIC-IV healthcare records, Adult Income, CelebA, and a novel FedFair-100 benchmark) demonstrate that CryptoFair-FL reduces fairness violations from 0.231 to 0.031 demographic parity difference while incurring only 2.3 times computational overhead compared to standard federated averaging. The framework successfully defends against attribute inference attacks, maintaining adversarial success probability below 0.05 across all tested configurations. These results establish a practical pathway for deploying fairness-aware federated learning in regulated industries requiring both privacy protection and algorithmic accountability.", "authors": ["Mohammed Himayath Ali", "Mohammed Aqib Abdullah", "Syed Muneer Hussain", "Mohammed Mudassir Uddin", "Shahnawaz Alam"], "categories": ["cs.CR", "cs.CL", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-01-18", "url": "https://arxiv.org/abs/2601.12447", "pdf_url": "https://arxiv.org/pdf/2601.12447v2", "arxiv_id": "2601.12447", "doi": "10.48550/arXiv.2601.12447", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4446}
{"id": "dd34e512aded99a453059457a24bc96f4ef2059c8c4be307676d17b86dc13330", "sources": ["arxiv", "semantic_scholar"], "title": "SynQP: A Framework and Metrics for Evaluating the Quality and Privacy Risk of Synthetic Data", "abstract": "The use of synthetic data in health applications raises privacy concerns, yet the lack of open frameworks for privacy evaluations has slowed its adoption. A major challenge is the absence of accessible benchmark datasets for evaluating privacy risks, due to difficulties in acquiring sensitive data. To address this, we introduce SynQP, an open framework for benchmarking privacy in synthetic data generation (SDG) using simulated sensitive data, ensuring that original data remains confidential. We also highlight the need for privacy metrics that fairly account for the probabilistic nature of machine learning models. As a demonstration, we use SynQP to benchmark CTGAN and propose a new identity disclosure risk metric that offers a more accurate estimation of privacy risks compared to existing approaches. Our work provides a critical tool for improving the transparency and reliability of privacy evaluations, enabling safer use of synthetic data in health-related applications. % In our quality evaluations, non-private models achieved near-perfect machine-learning efficacy \\(\\ge0.97\\). Our privacy assessments (Table II) reveal that DP consistently lowers both identity disclosure risk (SD-IDR) and membership-inference attack risk (SD-MIA), with all DP-augmented models staying below the 0.09 regulatory threshold. Code available at https://github.com/CAN-SYNH/SynQP", "authors": ["Bing Hu", "Yixin Li", "Asma Bahamyirou", "Helen Chen"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-01-17", "url": "https://arxiv.org/abs/2601.12124", "pdf_url": "https://arxiv.org/pdf/2601.12124v1", "arxiv_id": "2601.12124", "doi": "10.1109/PST65910.2025.11268831", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/CAN-SYNH/SynQP", "venue": "Conference on Privacy, Security and Trust", "quality_score": 0.6853}
{"id": "256fa62565650edb8d9a7b2bd7deb00ff8e4b723c902fb875aab34ed4e7c60b3", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Continual Learning for Privacy-Preserving Hospital Imaging Classification", "abstract": "Deep learning models for radiology interpretation increasingly rely on multi-institutional data, yet privacy regulations and distribution shift across hospitals limit central data pooling. Federated learning (FL) allows hospitals to collaboratively train models without sharing raw images, but current FL algorithms typically assume a static data distribution. In practice, hospitals experience continual evolution in case mix, annotation protocols, and imaging devices, which leads to catastrophic forgetting when models are updated sequentially. Federated continual learning (FCL) aims to reconcile these challenges but existing methods either ignore the stringent privacy constraints of healthcare or rely on replay buffers and public surrogate datasets that are difficult to justify in clinical settings. We study FCL for chest radiography classification in a setting where hospitals are clients that receive temporally evolving streams of cases and labels. We introduce DP-FedEPC (Differentially Private Federated Elastic Prototype Consolidation), a method that combines elastic weight consolidation (EWC), prototype-based rehearsal, and client-side differential privacy within a standard FedAvg framework. EWC constrains updates along parameters deemed important for previous tasks, while a memory of latent prototypes preserves class structure without storing raw images. Differentially private stochastic gradient descent (DP-SGD) at each client adds calibrated Gaussian noise to clipped gradients, providing formal privacy guarantees for individual radiographs.", "authors": ["Anay Sinhal", "Arpana Sinhal", "Amit Sinhal"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-01-11", "url": "https://arxiv.org/abs/2601.06742", "pdf_url": "https://arxiv.org/pdf/2601.06742v1", "arxiv_id": "2601.06742", "doi": "10.48550/arXiv.2601.06742", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4366}
{"id": "249ae56ea8ef0197779b1d67299e0edbafd7df0d9b6bb10f3492984df96d2a96", "sources": ["arxiv", "semantic_scholar"], "title": "Distributed Federated Learning by Alternating Periods of Training", "abstract": "Federated learning is a privacy-focused approach towards machine learning where models are trained on client devices with locally available data and aggregated at a central server. However, the dependence on a single central server is challenging in the case of a large number of clients and even poses the risk of a single point of failure. To address these critical limitations of scalability and fault-tolerance, we present a distributed approach to federated learning comprising multiple servers with inter-server communication capabilities. While providing a fully decentralized approach, the designed framework retains the core federated learning structure where each server is associated with a disjoint set of clients with server-client communication capabilities. We propose a novel DFL (Distributed Federated Learning) algorithm which uses alternating periods of local training on the client data followed by global training among servers. We show that the DFL algorithm, under a suitable choice of parameters, ensures that all the servers converge to a common model value within a small tolerance of the ideal model, thus exhibiting effective integration of local and global training models. Finally, we illustrate our theoretical claims through numerical simulations.", "authors": ["Shamik Bhattacharyya", "Rachel Kalpana Kalaimani"], "categories": ["cs.LG", "eess.SY"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2026-01-05", "url": "https://arxiv.org/abs/2601.01793", "pdf_url": "https://arxiv.org/pdf/2601.01793v1", "arxiv_id": "2601.01793", "doi": "10.48550/arXiv.2601.01793", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4297}
{"id": "dab73045a79aa8d9f66f8e2f615e930138fb6d186db579ea5b033c8cae8aa884", "sources": ["arxiv", "semantic_scholar"], "title": "FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs", "abstract": "Graph federated learning enables the collaborative extraction of high-order information from distributed subgraphs while preserving the privacy of raw data. However, graph data often exhibits overlap among different clients. Previous research has demonstrated certain benefits of overlapping data in mitigating data heterogeneity. However, the negative effects have not been explored, particularly in cases where the overlaps are imbalanced across clients. In this paper, we uncover the unfairness issue arising from imbalanced overlapping subgraphs through both empirical observations and theoretical reasoning. To address this issue, we propose FairGFL (FAIRness-aware subGraph Federated Learning), a novel algorithm that enhances cross-client fairness while maintaining model utility in a privacy-preserving manner. Specifically, FairGFL incorporates an interpretable weighted aggregation approach to enhance fairness across clients, leveraging privacy-preserving estimation of their overlapping ratios. Furthermore, FairGFL improves the tradeoff between model utility and fairness by integrating a carefully crafted regularizer into the federated composite loss function. Through extensive experiments on four benchmark graph datasets, we demonstrate that FairGFL outperforms four representative baseline algorithms in terms of both model utility and fairness.", "authors": ["Zihao Zhou", "Shusen Yang", "Fangyuan Zhao", "Xuebin Ren"], "categories": ["cs.LG", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-29", "url": "https://arxiv.org/abs/2512.23235", "pdf_url": "https://arxiv.org/pdf/2512.23235v1", "arxiv_id": "2512.23235", "doi": "10.1109/TPDS.2025.3649863", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Parallel and Distributed Systems", "quality_score": 0.4217}
{"id": "369fa8037bef31262bfa174bed0f212651b123b253fd6613df745e14ff20d62f", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning Based Decentralized Adaptive Intelligent Transmission Protocol for Privacy Preserving 6G Networks", "abstract": "The move to 6th Generation (6G) wireless networks creates new issues with privacy, scalability, and adaptability. The data-intensive nature of 6G is not handled well by older, centralized network models. A shift toward more secure and decentralized systems is therefore required. A new framework called the Federated Learning-based Decentralized Adaptive Intelligent Transmission Protocol (AITP) is proposed to meet these challenges. The AITP uses the distributed learning of Federated Learning (FL) within a decentralized system. Transmission parameters can be adjusted intelligently in real time. User privacy is maintained by keeping raw data on local edge devices. The protocol's performance was evaluated with mathematical modeling and detailed simulations. It was shown to be superior to traditional non-adaptive and centralized AI methods across several key metrics. These included latency, network throughput, energy efficiency, and robustness. The AITP is presented as a foundational technology for future 6G networks that supports a user-centric, privacy-first design. This study is a step forward for privacy-preserving research in 6G.", "authors": ["Ansar Ahmed"], "categories": ["cs.CR", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-20", "url": "https://arxiv.org/abs/2512.18432", "pdf_url": "https://arxiv.org/pdf/2512.18432v1", "arxiv_id": "2512.18432", "doi": "10.48550/arXiv.2512.18432", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4114}
{"id": "fb45664a0b7373a512087892aebbf7304a81af565c2115df54be4618fd6c7b7f", "sources": ["arxiv", "semantic_scholar"], "title": "Practical Framework for Privacy-Preserving and Byzantine-robust Federated Learning", "abstract": "Federated Learning (FL) allows multiple clients to collaboratively train a model without sharing their private data. However, FL is vulnerable to Byzantine attacks, where adversaries manipulate client models to compromise the federated model, and privacy inference attacks, where adversaries exploit client models to infer private data. Existing defenses against both backdoor and privacy inference attacks introduce significant computational and communication overhead, creating a gap between theory and practice. To address this, we propose ABBR, a practical framework for Byzantine-robust and privacy-preserving FL. We are the first to utilize dimensionality reduction to speed up the private computation of complex filtering rules in privacy-preserving FL. Additionally, we analyze the accuracy loss of vector-wise filtering in low-dimensional space and introduce an adaptive tuning strategy to minimize the impact of malicious models that bypass filtering on the global model. We implement ABBR with state-of-the-art Byzantine-robust aggregation rules and evaluate it on public datasets, showing that it runs significantly faster, has minimal communication overhead, and maintains nearly the same Byzantine-resilience as the baselines.", "authors": ["Baolei Zhang", "Minghong Fang", "Zhuqing Liu", "Biao Yi", "Peizhao Zhou", "Yuan Wang", "Tong Li", "Zheli Liu"], "categories": ["cs.CR", "cs.DC", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-19", "url": "https://arxiv.org/abs/2512.17254", "pdf_url": "https://arxiv.org/pdf/2512.17254v1", "arxiv_id": "2512.17254", "doi": "10.1109/TIFS.2025.3642546", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Information Forensics and Security", "quality_score": 0.4102}
{"id": "f21250ac80c887683145b5b6fcd255fe2baf7eb4be0771f6754ed1acb0e74253", "sources": ["arxiv", "semantic_scholar"], "title": "TrajSyn: Privacy-Preserving Dataset Distillation from Federated Model Trajectories for Server-Side Adversarial Training", "abstract": "Deep learning models deployed on edge devices are increasingly used in safety-critical applications. However, their vulnerability to adversarial perturbations poses significant risks, especially in Federated Learning (FL) settings where identical models are distributed across thousands of clients. While adversarial training is a strong defense, it is difficult to apply in FL due to strict client-data privacy constraints and the limited compute available on edge devices. In this work, we introduce TrajSyn, a privacy-preserving framework that enables effective server-side adversarial training by synthesizing a proxy dataset from the trajectories of client model updates, without accessing raw client data. We show that TrajSyn consistently improves adversarial robustness on image classification benchmarks with no extra compute burden on the client device.", "authors": ["Mukur Gupta", "Niharika Gupta", "Saifur Rahman", "Shantanu Pal", "Chandan Karmakar"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-17", "url": "https://arxiv.org/abs/2512.15123", "pdf_url": "https://arxiv.org/pdf/2512.15123v1", "arxiv_id": "2512.15123", "doi": "10.48550/arXiv.2512.15123", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4079}
{"id": "17a761b9491ecfee2dee24b3f716927b69882058bfa1b836b2b8d820c3e08b05", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Feature Valuation in Vertical Federated Learning Using Shapley-CMI and PSI Permutation", "abstract": "Federated Learning (FL) is an emerging machine learning paradigm that enables multiple parties to collaboratively train models without sharing raw data, ensuring data privacy. In Vertical FL (VFL), where each party holds different features for the same users, a key challenge is to evaluate the feature contribution of each party before any model is trained, particularly in the early stages when no model exists. To address this, the Shapley-CMI method was recently proposed as a model-free, information-theoretic approach to feature valuation using Conditional Mutual Information (CMI). However, its original formulation did not provide a practical implementation capable of computing the required permutations and intersections securely. This paper presents a novel privacy-preserving implementation of Shapley-CMI for VFL. Our system introduces a private set intersection (PSI) server that performs all necessary feature permutations and computes encrypted intersection sizes across discretized and encrypted ID groups, without the need for raw data exchange. Each party then uses these intersection results to compute Shapley-CMI values, computing the marginal utility of their features. Initial experiments confirm the correctness and privacy of the proposed system, demonstrating its viability for secure and efficient feature contribution estimation in VFL. This approach ensures data confidentiality, scales across multiple parties, and enables fair data valuation without requiring the sharing of raw data or training models.", "authors": ["Unai Laskurain", "Aitor Aguirre-Ortuzar", "Urko Zurutuza"], "categories": ["cs.CR", "cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-16", "url": "https://arxiv.org/abs/2512.14767", "pdf_url": "https://arxiv.org/pdf/2512.14767v1", "arxiv_id": "2512.14767", "doi": "10.1109/FLTA67013.2025.11336639", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2589}
{"id": "6784621d85097bb650ee4da247055ad4d5569d63b2e19fd9c159fd053613a16b", "sources": ["arxiv", "semantic_scholar"], "title": "PRIVEE: Privacy-Preserving Vertical Federated Learning Against Feature Inference Attacks", "abstract": "Vertical Federated Learning (VFL) enables collaborative model training across organizations that share common user samples but hold disjoint feature spaces. Despite its potential, VFL is susceptible to feature inference attacks, in which adversarial parties exploit shared confidence scores (i.e., prediction probabilities) during inference to reconstruct private input features of other participants. To counter this threat, we propose PRIVEE (PRIvacy-preserving Vertical fEderated lEarning), a novel defense mechanism named after the French word privée, meaning \"private.\" PRIVEE obfuscates confidence scores while preserving critical properties such as relative ranking and inter-score distances. Rather than exposing raw scores, PRIVEE shares only the transformed representations, mitigating the risk of reconstruction attacks without degrading model prediction accuracy. Extensive experiments show that PRIVEE achieves a threefold improvement in privacy protection compared to state-of-the-art defenses, while preserving full predictive performance against advanced feature inference attacks.", "authors": ["Sindhuja Madabushi", "Ahmad Faraz Khan", "Haider Ali", "Ananthram Swami", "Rui Ning", "Hongyi Wu", "Jin-Hee Cho"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-14", "url": "https://arxiv.org/abs/2512.12840", "pdf_url": "https://arxiv.org/pdf/2512.12840v1", "arxiv_id": "2512.12840", "doi": "10.48550/arXiv.2512.12840", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4045}
{"id": "d1c6360605c27c64f52794a8524df73e9628d9297a0e845226cb11450164d43e", "sources": ["arxiv", "semantic_scholar"], "title": "Semantic-Constrained Federated Aggregation: Convergence Theory and Privacy-Utility Bounds for Knowledge-Enhanced Distributed Learning", "abstract": "Federated learning enables collaborative model training across distributed data sources but suffers from slow convergence under non-IID data conditions. Existing solutions employ algorithmic modifications treating all client updates identically, ignoring semantic validity. We introduce Semantic-Constrained Federated Aggregation (SCFA), a theoretically-grounded framework incorporating domain knowledge constraints into distributed optimization. We prove SCFA achieves convergence rate O(1/sqrt(T) + rho) where rho represents constraint violation rate, establishing the first convergence theory for constraint-based federated learning. Our analysis shows constraints reduce effective data heterogeneity by 41% and improve privacy-utility tradeoffs through hypothesis space reduction by factor theta=0.37. Under (epsilon,delta)-differential privacy with epsilon=10, constraint regularization maintains utility within 3.7% of non-private baseline versus 12.1% degradation for standard federated learning, representing 2.7x improvement. We validate our framework on manufacturing predictive maintenance using Bosch production data with 1.18 million samples and 968 sensor features, constructing knowledge graphs encoding 3,000 constraints from ISA-95 and MASON ontologies. Experiments demonstrate 22% faster convergence, 41.3% model divergence reduction, and constraint violation thresholds where rho<0.05 maintains 90% optimal performance while rho>0.18 causes catastrophic failure. Our theoretical predictions match empirical observations with R^2>0.90 across convergence, privacy, and violation-performance relationships.", "authors": ["Jahidul Arafat"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-12", "url": "https://arxiv.org/abs/2512.15759", "pdf_url": "https://arxiv.org/pdf/2512.15759v1", "arxiv_id": "2512.15759", "doi": "10.48550/arXiv.2512.15759", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4022}
{"id": "54143657b79a6b7ce57b62b017c9dbbd92846371d8c8ea09ef49de0dd108e458", "sources": ["arxiv", "semantic_scholar"], "title": "A Privacy-Preserving Cloud Architecture for Distributed Machine Learning at Scale", "abstract": "Distributed machine learning systems require strong privacy guarantees, verifiable compliance, and scalable deployment across heterogeneous and multi-cloud environments. This work introduces a cloud-native privacy-preserving architecture that integrates federated learning, differential privacy, zero-knowledge compliance proofs, and adaptive governance powered by reinforcement learning. The framework supports secure model training and inference without centralizing sensitive data, while enabling cryptographically verifiable policy enforcement across institutions and cloud platforms. A full prototype deployed across hybrid Kubernetes clusters demonstrates reduced membership-inference risk, consistent enforcement of formal privacy budgets, and stable model performance under differential privacy. Experimental evaluation across multi-institution workloads shows that the architecture maintains utility with minimal overhead while providing continuous, risk-aware governance. The proposed framework establishes a practical foundation for deploying trustworthy and compliant distributed machine learning systems at scale.", "authors": ["Vinoth Punniyamoorthy", "Ashok Gadi Parthi", "Mayilsamy Palanigounder", "Ravi Kiran Kodali", "Bikesh Kumar", "Kabilan Kannan"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-11", "url": "https://arxiv.org/abs/2512.10341", "pdf_url": "https://arxiv.org/pdf/2512.10341v1", "arxiv_id": "2512.10341", "doi": "10.17577/IJERTV14IS110277", "citation_count": 16, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.401}
{"id": "35a1d4c8e077afb97000dbe24790e7655e4d1f67413c655e8d20f1d887c5e866", "sources": ["arxiv", "semantic_scholar"], "title": "How to Train Private Clinical Language Models: A Comparative Study of Privacy-Preserving Pipelines for ICD-9 Coding", "abstract": "Large language models trained on clinical text risk exposing sensitive patient information, yet differential privacy (DP) methods often severely degrade the diagnostic accuracy needed for deployment. Despite rapid progress in DP optimisation and text generation, it remains unclear which privacy-preserving strategy actually works best for clinical language tasks. We present the first systematic head-to-head comparison of four training pipelines for automated diagnostic coding from hospital discharge summaries. All pipelines use identical 1B-parameter models and matched privacy budgets to predict ICD-9 codes. At moderate and relaxed privacy budgets ($\\varepsilon \\in \\{4, 6\\}$), knowledge distillation from DP-trained teachers outperforms both direct DP-SGD and DP-synthetic data training, recovering up to 63\\% of the non-private performance whilst maintaining strong empirical privacy (membership-inference AUC $\\approx$ 0.5). These findings expose large differences in the privacy-utility trade-off across architectures and identify knowledge distillation as the most practical route to privacy-preserving clinical NLP.", "authors": ["Mathieu Dufour", "Andrew Duncan"], "categories": ["cs.LG", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-18", "url": "https://arxiv.org/abs/2511.14936", "pdf_url": "https://arxiv.org/pdf/2511.14936v1", "arxiv_id": "2511.14936", "doi": "10.48550/arXiv.2511.14936", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3747}
{"id": "a3460bacdf039d45a0bae4fa825ebb9339ab04107c2ad2a71be897397d8e4d32", "sources": ["arxiv", "semantic_scholar"], "title": "Scaling Law Analysis in Federated Learning: How to Select the Optimal Model Size?", "abstract": "The recent success of large language models (LLMs) has sparked a growing interest in training large-scale models. As the model size continues to scale, concerns are growing about the depletion of high-quality, well-curated training data. This has led practitioners to explore training approaches like Federated Learning (FL), which can leverage the abundant data on edge devices while maintaining privacy. However, the decentralization of training datasets in FL introduces challenges to scaling large models, a topic that remains under-explored. This paper fills this gap and provides qualitative insights on generalizing the previous model scaling experience to federated learning scenarios. Specifically, we derive a PAC-Bayes (Probably Approximately Correct Bayesian) upper bound for the generalization error of models trained with stochastic algorithms in federated settings and quantify the impact of distributed training data on the optimal model size by finding the analytic solution of model size that minimizes this bound. Our theoretical results demonstrate that the optimal model size has a negative power law relationship with the number of clients if the total training compute is unchanged. Besides, we also find that switching to FL with the same training compute will inevitably reduce the upper bound of generalization performance that the model can achieve through training, and that estimating the optimal model size in federated scenarios should depend on the average training compute across clients. Furthermore, we also empirically validate the correctness of our results with extensive training runs on different models, network settings, and datasets.", "authors": ["Xuanyu Chen", "Nan Yang", "Shuai Wang", "Dong Yuan"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-15", "url": "https://arxiv.org/abs/2511.12188", "pdf_url": "https://arxiv.org/pdf/2511.12188v1", "arxiv_id": "2511.12188", "doi": "10.48550/arXiv.2511.12188", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "AAAI Conference on Artificial Intelligence", "quality_score": 0.3713}
{"id": "746a30ebd051766c4c19229a501edc492c59c96b2c23d08f52249d9a940113da", "sources": ["arxiv", "semantic_scholar"], "title": "Bridging Local and Federated Data Normalization in Federated Learning: A Privacy-Preserving Approach", "abstract": "Data normalization is a crucial preprocessing step for enhancing model performance and training stability. In federated learning (FL), where data remains distributed across multiple parties during collaborative model training, normalization presents unique challenges due to the decentralized and often heterogeneous nature of the data. Traditional methods rely on either independent client-side processing, i.e., local normalization, or normalizing the entire dataset before distributing it to parties, i.e., pooled normalization. Local normalization can be problematic when data distributions across parties are non-IID, while the pooled normalization approach conflicts with the decentralized nature of FL. In this paper, we explore the adaptation of widely used normalization techniques to FL and define the term federated normalization. Federated normalization simulates pooled normalization by enabling the collaborative exchange of normalization parameters among parties. Thus, it achieves performance on par with pooled normalization without compromising data locality. However, sharing normalization parameters such as the mean introduces potential privacy risks, which we further mitigate through a robust privacy-preserving solution. Our contributions include: (i) We systematically evaluate the impact of various federated and local normalization techniques in heterogeneous FL scenarios, (ii) We propose a novel homomorphically encrypted $k$-th ranked element (and median) calculation tailored for the federated setting, enabling secure and efficient federated normalization, (iii) We propose privacy-preserving implementations of widely used normalization techniques for FL, leveraging multiparty fully homomorphic encryption (MHE).", "authors": ["Melih Coşğun", "Mert Gençtürk", "Sinem Sav"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-14", "url": "https://arxiv.org/abs/2511.11249", "pdf_url": "https://arxiv.org/pdf/2511.11249v1", "arxiv_id": "2511.11249", "doi": "10.48550/arXiv.2511.11249", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3701}
{"id": "008508bd50500f1773b2c3652cea6ae756cc9292fda6ae32b9ecbf1ccc15152a", "sources": ["arxiv", "semantic_scholar"], "title": "Experiences Building Enterprise-Level Privacy-Preserving Federated Learning to Power AI for Science", "abstract": "Federated learning (FL) is a promising approach to enabling collaborative model training without centralized data sharing, a crucial requirement in scientific domains where data privacy, ownership, and compliance constraints are critical. However, building user-friendly enterprise-level FL frameworks that are both scalable and privacy-preserving remains challenging, especially when bridging the gap between local prototyping and distributed deployment across heterogeneous client computing infrastructures. In this paper, based on our experiences building the Advanced Privacy-Preserving Federated Learning (APPFL) framework, we present our vision for an enterprise-grade, privacy-preserving FL framework designed to scale seamlessly across computing environments. We identify several key capabilities that such a framework must provide: (1) Scalable local simulation and prototyping to accelerate experimentation and algorithm design; (2) seamless transition from simulation to deployment; (3) distributed deployment across diverse, real-world infrastructures, from personal devices to cloud clusters and HPC systems; (4) multi-level abstractions that balance ease of use and research flexibility; and (5) comprehensive privacy and security through techniques such as differential privacy, secure aggregation, robust authentication, and confidential computing. We further discuss architectural designs to realize these goals. This framework aims to bridge the gap between research prototypes and enterprise-scale deployment, enabling scalable, reliable, and privacy-preserving AI for science.", "authors": ["Zilinghan Li", "Aditya Sinha", "Yijiang Li", "Kyle Chard", "Kibaek Kim", "Ravi Madduri"], "categories": ["cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-12", "url": "https://arxiv.org/abs/2511.08998", "pdf_url": "https://arxiv.org/pdf/2511.08998v1", "arxiv_id": "2511.08998", "doi": "10.1109/TPS-ISA67132.2025.00047", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Trust, Privacy and Security in Intelligent Systems and Applications", "quality_score": 0.3678}
{"id": "d00643196e53fc4947d7e1f0ac1812ac2d8fe5f0a40dfa491988bdcbacf6d8c0", "sources": ["arxiv", "semantic_scholar"], "title": "MedHE: Communication-Efficient Privacy-Preserving Federated Learning with Adaptive Gradient Sparsification for Healthcare", "abstract": "Healthcare federated learning requires strong privacy guarantees while maintaining computational efficiency across resource-constrained medical institutions. This paper presents MedHE, a novel framework combining adaptive gradient sparsification with CKKS homomorphic encryption to enable privacy-preserving collaborative learning on sensitive medical data. Our approach introduces a dynamic threshold mechanism with error compensation for top-k gradient selection, achieving 97.5 percent communication reduction while preserving model utility. We provide formal security analysis under Ring Learning with Errors assumptions and demonstrate differential privacy guarantees with epsilon less than or equal to 1.0. Statistical testing across 5 independent trials shows MedHE achieves 89.5 percent plus or minus 0.8 percent accuracy, maintaining comparable performance to standard federated learning (p=0.32) while reducing communication from 1277 MB to 32 MB per training round. Comprehensive evaluation demonstrates practical feasibility for real-world medical deployments with HIPAA compliance and scalability to 100 plus institutions.", "authors": ["Farjana Yesmin"], "categories": ["cs.CR", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-12", "url": "https://arxiv.org/abs/2511.09043", "pdf_url": "https://arxiv.org/pdf/2511.09043v1", "arxiv_id": "2511.09043", "doi": "10.48550/arXiv.2511.09043", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3678}
{"id": "ff120f7f2b64ad31798a8dad0ec03cb520f2a3d16bdb4666ae3d4838642eb02c", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Federated Learning for Fair and Efficient Urban Traffic Optimization", "abstract": "The optimization of urban traffic is threatened by the complexity of achieving a balance between transport efficiency and the maintenance of privacy, as well as the equitable distribution of traffic based on socioeconomically diverse neighborhoods. Current centralized traffic management schemes invade user location privacy and further entrench traffic disparity by offering disadvantaged route suggestions, whereas current federated learning frameworks do not consider fairness constraints in multi-objective traffic settings. This study presents a privacy-preserving federated learning framework, termed FedFair-Traffic, that jointly and simultaneously optimizes travel efficiency, traffic fairness, and differential privacy protection. This is the first attempt to integrate three conflicting objectives to improve urban transportation systems. The proposed methodology enables collaborative learning between related vehicles with data locality by integrating Graph Neural Networks with differential privacy mechanisms ($ε$-privacy guarantees) and Gini coefficient-based fair constraints using multi-objective optimization. The framework uses federated aggregation methods of gradient clipping and noise injection to provide differential privacy and optimize Pareto-efficient solutions for the efficiency-fairness tradeoff. Real-world comprehensive experiments on the METR-LA traffic dataset showed that FedFair-Traffic can reduce the average travel time by 7\\% (14.2 minutes) compared with their centralized baselines, promote traffic fairness by 73\\% (Gini coefficient, 0.78), and offer high privacy protection (privacy score, 0.8) with an 89\\% reduction in communication overhead. These outcomes demonstrate that FedFair-Traffic is a scalable privacy-aware smart city infrastructure with possible use-cases in metropolitan traffic flow control and federated transportation networks.", "authors": ["Rathin Chandra Shit", "Sharmila Subudhi"], "categories": ["cs.LG", "cs.AI", "cs.NI", "eess.SY"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2025-11-09", "url": "https://arxiv.org/abs/2511.06363", "pdf_url": "https://arxiv.org/pdf/2511.06363v1", "arxiv_id": "2511.06363", "doi": "10.48550/arXiv.2511.06363", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3644}
{"id": "67f9181eaa6f7f0327c766830d429716933985de635562934ecd65cd0d04729a", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Cyber Defense: Privacy-Preserving Ransomware Detection Across Distributed Systems", "abstract": "Detecting malware, especially ransomware, is essential to securing today's interconnected ecosystems, including cloud storage, enterprise file-sharing, and database services. Training high-performing artificial intelligence (AI) detectors requires diverse datasets, which are often distributed across multiple organizations, making centralization necessary. However, centralized learning is often impractical due to security, privacy regulations, data ownership issues, and legal barriers to cross-organizational sharing. Compounding this challenge, ransomware evolves rapidly, demanding models that are both robust and adaptable. In this paper, we evaluate Federated Learning (FL) using the Sherpa.ai FL platform, which enables multiple organizations to collaboratively train a ransomware detection model while keeping raw data local and secure. This paradigm is particularly relevant for cybersecurity companies (including both software and hardware vendors) that deploy ransomware detection or firewall systems across millions of endpoints. In such environments, data cannot be transferred outside the customer's device due to strict security, privacy, or regulatory constraints. Although FL applies broadly to malware threats, we validate the approach using the Ransomware Storage Access Patterns (RanSAP) dataset. Our experiments demonstrate that FL improves ransomware detection accuracy by a relative 9% over server-local models and achieves performance comparable to centralized training. These results indicate that FL offers a scalable, high-performing, and privacy-preserving framework for proactive ransomware detection across organizational and regulatory boundaries.", "authors": ["Daniel M. Jimenez-Gutierrez", "Enrique Zuazua", "Joaquin Del Rio", "Oleksii Sliusarenko", "Xabi Uribe-Etxebarria"], "categories": ["cs.CR", "cs.AI", "cs.DC", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-03", "url": "https://arxiv.org/abs/2511.01583", "pdf_url": "https://arxiv.org/pdf/2511.01583v1", "arxiv_id": "2511.01583", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2275}
{"id": "26d123f12234e4b8194c6cad3df3ad4940aca0bda53a6754542b763a8bbfa43f", "sources": ["arxiv", "semantic_scholar"], "title": "Incentive-Based Federated Learning: Architectural Elements and Future Directions", "abstract": "Federated learning promises to revolutionize machine learning by enabling collaborative model training without compromising data privacy. However, practical adaptability can be limited by critical factors, such as the participation dilemma. Participating entities are often unwilling to contribute to a learning system unless they receive some benefits, or they may pretend to participate and free-ride on others. This chapter identifies the fundamental challenges in designing incentive mechanisms for federated learning systems. It examines how foundational concepts from economics and game theory can be applied to federated learning, alongside technology-driven solutions such as blockchain and deep reinforcement learning. This work presents a comprehensive taxonomy that thoroughly covers both centralized and decentralized architectures based on the aforementioned theoretical concepts. Furthermore, the concepts described are presented from an application perspective, covering emerging industrial applications, including healthcare, smart infrastructure, vehicular networks, and blockchain-based decentralized systems. Through this exploration, this chapter demonstrates that well-designed incentive mechanisms are not merely optional features but essential components for the practical success of federated learning. This analysis reveals both the promising solutions that have emerged and the significant challenges that remain in building truly sustainable, fair, and robust federated learning ecosystems.", "authors": ["Chanuka A. S. Hewa Kaluannakkage", "Rajkumar Buyya"], "categories": ["cs.LG", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-16", "url": "https://arxiv.org/abs/2510.14208", "pdf_url": "https://arxiv.org/pdf/2510.14208v2", "arxiv_id": "2510.14208", "doi": "10.48550/arXiv.2510.14208", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3369}
{"id": "0c164c18ce3faf3e70de3f9ab142e3581b6e55ac8a845ebd3f52b80b45b7f5ca", "sources": ["arxiv", "semantic_scholar"], "title": "Permutation-Invariant Representation Learning for Robust and Privacy-Preserving Feature Selection", "abstract": "Feature selection eliminates redundancy among features to improve downstream task performance while reducing computational overhead. Existing methods often struggle to capture intricate feature interactions and adapt across diverse application scenarios. Recent advances employ generative intelligence to alleviate these drawbacks. However, these methods remain constrained by permutation sensitivity in embedding and reliance on convexity assumptions in gradient-based search. To address these limitations, our initial work introduces a novel framework that integrates permutation-invariant embedding with policy-guided search. Although effective, it still left opportunities to adapt to realistic distributed scenarios. In practice, data across local clients is highly imbalanced, heterogeneous and constrained by strict privacy regulations, limiting direct sharing. These challenges highlight the need for a framework that can integrate feature selection knowledge across clients without exposing sensitive information. In this extended journal version, we advance the framework from two perspectives: 1) developing a privacy-preserving knowledge fusion strategy to derive a unified representation space without sharing sensitive raw data. 2) incorporating a sample-aware weighting strategy to address distributional imbalance among heterogeneous local clients. Extensive experiments validate the effectiveness, robustness, and efficiency of our framework. The results further demonstrate its strong generalization ability in federated learning scenarios. The code and data are publicly available: https://anonymous.4open.science/r/FedCAPS-08BF.", "authors": ["Rui Liu", "Tao Zhe", "Yanjie Fu", "Feng Xia", "Ted Senator", "Dongjie Wang"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-07", "url": "https://arxiv.org/abs/2510.05535", "pdf_url": "https://arxiv.org/pdf/2510.05535v3", "arxiv_id": "2510.05535", "doi": "10.48550/arXiv.2510.05535", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3266}
{"id": "2e382b06aaac04ebb1db958140c100a4246f7d86ca1695e5abdbbea70cd88ec8", "sources": ["arxiv", "semantic_scholar"], "title": "Beyond Static Knowledge Messengers: Towards Adaptive, Fair, and Scalable Federated Learning for Medical AI", "abstract": "Medical AI faces challenges in privacy-preserving collaborative learning while ensuring fairness across heterogeneous healthcare institutions. Current federated learning approaches suffer from static architectures, slow convergence (45-73 rounds), fairness gaps marginalizing smaller institutions, and scalability constraints (15-client limit). We propose Adaptive Fair Federated Learning (AFFL) through three innovations: (1) Adaptive Knowledge Messengers dynamically scaling capacity based on heterogeneity and task complexity, (2) Fairness-Aware Distillation using influence-weighted aggregation, and (3) Curriculum-Guided Acceleration reducing rounds by 60-70%. Our theoretical analysis provides convergence guarantees with epsilon-fairness bounds, achieving O(T^{-1/2}) + O(H_max/T^{3/4}) rates. Projected results show 55-75% communication reduction, 56-68% fairness improvement, 34-46% energy savings, and 100+ institution support. The framework enables multi-modal integration across imaging, genomics, EHR, and sensor data while maintaining HIPAA/GDPR compliance. We propose MedFedBench benchmark suite for standardized evaluation across six healthcare dimensions: convergence efficiency, institutional fairness, privacy preservation, multi-modal integration, scalability, and clinical deployment readiness. Economic projections indicate 400-800% ROI for rural hospitals and 15-25% performance gains for academic centers. This work presents a seven-question research agenda, 24-month implementation roadmap, and pathways toward democratizing healthcare AI.", "authors": ["Jahidul Arafat", "Fariha Tasmin", "Sanjaya Poudel", "Iftekhar Haider"], "categories": ["cs.CY", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-05", "url": "https://arxiv.org/abs/2510.06259", "pdf_url": "https://arxiv.org/pdf/2510.06259v2", "arxiv_id": "2510.06259", "doi": "10.48550/arXiv.2510.06259", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3243}
{"id": "fdfb28817961b30271c450d5c874cfc1a5327f200d39329742a8ab8374dd143c", "sources": ["arxiv", "semantic_scholar"], "title": "A Lightweight Federated Learning Approach for Privacy-Preserving Botnet Detection in IoT", "abstract": "The rapid growth of the Internet of Things (IoT) has expanded opportunities for innovation but also increased exposure to botnet-driven cyberattacks. Conventional detection methods often struggle with scalability, privacy, and adaptability in resource-constrained IoT environments. To address these challenges, we present a lightweight and privacy-preserving botnet detection framework based on federated learning. This approach enables distributed devices to collaboratively train models without exchanging raw data, thus maintaining user privacy while preserving detection accuracy. A communication-efficient aggregation strategy is introduced to reduce overhead, ensuring suitability for constrained IoT networks. Experiments on benchmark IoT botnet datasets demonstrate that the framework achieves high detection accuracy while substantially reducing communication costs. These findings highlight federated learning as a practical path toward scalable, secure, and privacy-aware intrusion detection for IoT ecosystems.", "authors": ["Taha M. Mahmoud", "Naima Kaabouch"], "categories": ["cs.LG", "cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-03", "url": "https://arxiv.org/abs/2510.03513", "pdf_url": "https://arxiv.org/pdf/2510.03513v1", "arxiv_id": "2510.03513", "doi": "10.1109/ACDSA65407.2025.11165820", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)", "quality_score": 0.322}
{"id": "91ecaef0143516788f02c3916143352c1e0e02c9482b4538405dbb74c88dac10", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy Preserved Federated Learning with Attention-Based Aggregation for Biometric Recognition", "abstract": "Because biometric data is sensitive, centralized training poses a privacy risk, even though biometric recognition is essential for contemporary applications. Federated learning (FL), which permits decentralized training, provides a privacy-preserving substitute. Conventional FL, however, has trouble with interpretability and heterogeneous data (non-IID). In order to handle non-IID biometric data, this framework adds an attention mechanism at the central server that weights local model updates according to their significance. Differential privacy and secure update protocols safeguard data while preserving accuracy. The A3-FL framework is evaluated in this study using FVC2004 fingerprint data, with each client's features extracted using a Siamese Convolutional Neural Network (Siamese-CNN). By dynamically modifying client contributions, the attention mechanism increases the accuracy of the global model.The accuracy, convergence speed, and robustness of the A3-FL framework are superior to those of standard FL (FedAvg) and static baselines, according to experimental evaluations using fingerprint data (FVC2004). The accuracy of the attention-based approach was 0.8413, while FedAvg, Local-only, and Centralized approaches were 0.8164, 0.7664, and 0.7997, respectively. Accuracy stayed high at 0.8330 even with differential privacy. A scalable and privacy-sensitive biometric system for secure and effective recognition in dispersed environments is presented in this work.", "authors": ["Kassahun Azezew", "Minyechil Alehegn", "Tsega Asresa", "Bitew Mekuria", "Tizazu Bayh", "Ayenew Kassie", "Amsalu Tesema", "Animut Embiyale"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-01", "url": "https://arxiv.org/abs/2510.01113", "pdf_url": "https://arxiv.org/pdf/2510.01113v1", "arxiv_id": "2510.01113", "doi": "10.48550/arXiv.2510.01113", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3197}
{"id": "f89a10f5374d1eec21af848e01ed66b34f4269beeb20ccaf536f4d206056a972", "sources": ["arxiv", "semantic_scholar"], "title": "OptimES: Optimizing Federated Learning Using Remote Embeddings for Graph Neural Networks", "abstract": "Graph Neural Networks (GNNs) have experienced rapid advancements in recent years due to their ability to learn meaningful representations from graph data structures. However, in most real-world settings, such as financial transaction networks and healthcare networks, this data is localized to different data owners and cannot be aggregated due to privacy concerns. Federated Learning (FL) has emerged as a viable machine learning approach for training a shared model that iteratively aggregates local models trained on decentralized data. This addresses privacy concerns while leveraging parallelism. State-of-the-art methods enhance the privacy-respecting convergence accuracy of federated GNN training by sharing remote embeddings of boundary vertices through a server (EmbC). However, they are limited by diminished performance due to large communication costs. In this article, we propose OptimES, an optimized federated GNN training framework that employs remote neighbourhood pruning, overlapping the push of embeddings to the server with local training, and dynamic pulling of embeddings to reduce network costs and training time. We perform a rigorous evaluation of these strategies for four common graph datasets with up to $111M$ vertices and $1.8B$ edges. We see that a modest drop in per-round accuracy due to the preemptive push of embeddings is out-stripped by the reduction in per-round training time for large and dense graphs like Reddit and Products, converging up to $\\approx 3.5\\times$ faster than EmbC and giving up to $\\approx16\\%$ better accuracy than the default federated GNN learning. While accuracy improvements over default federated GNNs are modest for sparser graphs like Arxiv and Papers, they achieve the target accuracy about $\\approx11\\times$ faster than EmbC.", "authors": ["Pranjal Naman", "Yogesh Simmhan"], "categories": ["cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-26", "url": "https://arxiv.org/abs/2509.22922", "pdf_url": "https://arxiv.org/pdf/2509.22922v1", "arxiv_id": "2509.22922", "doi": "10.48550/arXiv.2506.12425", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.314}
{"id": "af5febb1ba0c3db42969fca9c8cb9ffb947292025c2b7a00ea0af8692c3ff639", "sources": ["arxiv", "semantic_scholar"], "title": "Distribution-Controlled Client Selection to Improve Federated Learning Strategies", "abstract": "Federated learning (FL) is a distributed learning paradigm that allows multiple clients to jointly train a shared model while maintaining data privacy. Despite its great potential for domains with strict data privacy requirements, the presence of data imbalance among clients is a thread to the success of FL, as it causes the performance of the shared model to decrease. To address this, various studies have proposed enhancements to existing FL strategies, particularly through client selection methods that mitigate the detrimental effects of data imbalance. In this paper, we propose an extension to existing FL strategies, which selects active clients that best align the current label distribution with one of two target distributions, namely a balanced distribution or the federations combined label distribution. Subsequently, we empirically verify the improvements through our distribution-controlled client selection on three common FL strategies and two datasets. Our results show that while aligning the label distribution with a balanced distribution yields the greatest improvements facing local imbalance, alignment with the federation's combined label distribution is superior for global imbalance.", "authors": ["Christoph Düsing", "Philipp Cimiano"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-25", "url": "https://arxiv.org/abs/2509.20877", "pdf_url": "https://arxiv.org/pdf/2509.20877v1", "arxiv_id": "2509.20877", "doi": "10.48550/arXiv.2509.20877", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3128}
{"id": "b9403695e667aa4370f4026e9e2fa38b56f6460f11a86f48e61df658e0f15888", "sources": ["arxiv", "semantic_scholar"], "title": "PQFed: A Privacy-Preserving Quality-Controlled Federated Learning Framework", "abstract": "Federated learning enables collaborative model training without sharing raw data, but data heterogeneity consistently challenges the performance of the global model. Traditional optimization methods often rely on collaborative global model training involving all clients, followed by local adaptation to improve individual performance. In this work, we focus on early-stage quality control and propose PQFed, a novel privacy-preserving personalized federated learning framework that designs customized training strategies for each client prior to the federated training process. PQFed extracts representative features from each client's raw data and applies clustering techniques to estimate inter-client dataset similarity. Based on these similarity estimates, the framework implements a client selection strategy that enables each client to collaborate with others who have compatible data distributions. We evaluate PQFed on two benchmark datasets, CIFAR-10 and MNIST, integrated with three existing federated learning algorithms. Experimental results show that PQFed consistently improves the target client's model performance, even with a limited number of participants. We further benchmark PQFed against a baseline cluster-based algorithm, IFCA, and observe that PQFed also achieves better performance in low-participation scenarios. These findings highlight PQFed's scalability and effectiveness in personalized federated learning settings.", "authors": ["Weiqi Yue", "Wenbiao Li", "Yuzhou Jiang", "Anisa Halimi", "Roger French", "Erman Ayday"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-25", "url": "https://arxiv.org/abs/2509.21704", "pdf_url": "https://arxiv.org/pdf/2509.21704v1", "arxiv_id": "2509.21704", "doi": "10.48550/arXiv.2509.21704", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3128}
{"id": "2c2d34df8f377d60949ff8238de2ca2bdc353337a56af78bb502e7fedb15f1bc", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Privacy-Preserving and Heterogeneity-aware Split Federated Learning via Probabilistic Masking", "abstract": "Split Federated Learning (SFL) has emerged as an efficient alternative to traditional Federated Learning (FL) by reducing client-side computation through model partitioning. However, exchanging of intermediate activations and model updates introduces significant privacy risks, especially from data reconstruction attacks that recover original inputs from intermediate representations. Existing defenses using noise injection often degrade model performance. To overcome these challenges, we present PM-SFL, a scalable and privacy-preserving SFL framework that incorporates Probabilistic Mask training to add structured randomness without relying on explicit noise. This mitigates data reconstruction risks while maintaining model utility. To address data heterogeneity, PM-SFL employs personalized mask learning that tailors submodel structures to each client's local data. For system heterogeneity, we introduce a layer-wise knowledge compensation mechanism, enabling clients with varying resources to participate effectively under adaptive model splitting. Theoretical analysis confirms its privacy protection, and experiments on image and wireless sensing tasks demonstrate that PM-SFL consistently improves accuracy, communication efficiency, and robustness to privacy attacks, with particularly strong performance under data and system heterogeneity.", "authors": ["Xingchen Wang", "Feijie Wu", "Chenglin Miao", "Tianchun Li", "Haoyu Hu", "Qiming Cao", "Jing Gao", "Lu Su"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-18", "url": "https://arxiv.org/abs/2509.14603", "pdf_url": "https://arxiv.org/pdf/2509.14603v2", "arxiv_id": "2509.14603", "doi": "10.1145/3770854.3780255", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Knowledge Discovery and Data Mining", "quality_score": 0.3048}
{"id": "6a27b50e0db2a9d1a05cbc4f83c1ba0e8054a7680ccbba80b5f62bce0ea4b08d", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy Preserving In-Context-Learning Framework for Large Language Models", "abstract": "Large language models (LLMs) have significantly transformed natural language understanding and generation, but they raise privacy concerns due to potential exposure of sensitive information. Studies have highlighted the risk of information leakage, where adversaries can extract sensitive information embedded in the prompts. In this work, we introduce a novel private prediction framework for generating high-quality synthetic text with strong privacy guarantees. Our approach leverages the Differential Privacy (DP) framework to ensure worst-case theoretical bounds on information leakage without requiring any fine-tuning of the underlying models. The proposed method performs inference on private records and aggregates the resulting per-token output distributions. This enables the generation of longer and coherent synthetic text while maintaining privacy guarantees. Additionally, we propose a simple blending operation that combines private and public inference to further enhance utility. Empirical evaluations demonstrate that our approach outperforms previous state-of-the-art methods on in-context-learning (ICL) tasks, making it a promising direction for privacy-preserving text generation while maintaining high utility. Our code is available at https://github.com/bhusalb/privacy-preserving-icl.", "authors": ["Bishnu Bhusal", "Manoj Acharya", "Ramneet Kaur", "Colin Samplawski", "Anirban Roy", "Adam D. Cobb", "Rohit Chadha", "Susmit Jha"], "categories": ["cs.LG", "cs.CL", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-17", "url": "https://arxiv.org/abs/2509.13625", "pdf_url": "https://arxiv.org/pdf/2509.13625v4", "arxiv_id": "2509.13625", "doi": "10.1609/aaai.v40i42.40838", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/bhusalb/privacy-preserving-icl", "venue": "AAAI Conference on Artificial Intelligence", "quality_score": 0.4693}
{"id": "aa16005083bbdfb3b9fdc4c12023a58a39e592921c9fa1d5d7bd3f51d6975f61", "sources": ["arxiv", "semantic_scholar"], "title": "Efficient Byzantine-Robust Privacy-Preserving Federated Learning via Dimension Compression", "abstract": "Federated Learning (FL) allows collaborative model training across distributed clients without sharing raw data, thus preserving privacy. However, the system remains vulnerable to privacy leakage from gradient updates and Byzantine attacks from malicious clients. Existing solutions face a critical trade-off among privacy preservation, Byzantine robustness, and computational efficiency. We propose a novel scheme that effectively balances these competing objectives by integrating homomorphic encryption with dimension compression based on the Johnson-Lindenstrauss transformation. Our approach employs a dual-server architecture that enables secure Byzantine defense in the ciphertext domain while dramatically reducing computational overhead through gradient compression. The dimension compression technique preserves the geometric relationships necessary for Byzantine defence while reducing computation complexity from $O(dn)$ to $O(kn)$ cryptographic operations, where $k \\ll d$. Extensive experiments across diverse datasets demonstrate that our approach maintains model accuracy comparable to non-private FL while effectively defending against Byzantine clients comprising up to $40\\%$ of the network.", "authors": ["Xian Qin", "Xue Yang", "Xiaohu Tang"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-15", "url": "https://arxiv.org/abs/2509.11870", "pdf_url": "https://arxiv.org/pdf/2509.11870v1", "arxiv_id": "2509.11870", "doi": "10.1109/TIFS.2026.3671104", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Information Forensics and Security", "quality_score": 0.3014}
{"id": "33721fdbd37020ef5846cac8bdac069e23fd913e50698b80415db2abb83c63eb", "sources": ["arxiv", "semantic_scholar"], "title": "Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization", "abstract": "Distributed and federated learning are essential paradigms for training models across decentralized data sources while preserving privacy, yet communication overhead remains a major bottleneck. This dissertation explores strategies to improve communication efficiency, focusing on model compression, local training, and personalization. We establish a unified framework for biased and unbiased compression operators with convergence guarantees, then propose adaptive local training strategies that incorporate personalization to accelerate convergence and mitigate client drift. In particular, Scafflix balances global and personalized objectives, achieving superior performance under both IID and non-IID settings. We further introduce privacy-preserving pruning frameworks that optimize sparsity while minimizing communication costs, with Cohort-Squeeze leveraging hierarchical aggregation to reduce cross-device overhead. Finally, SymWanda, a symmetric post-training pruning method, enhances robustness under high sparsity and maintains accuracy without retraining. Extensive experiments on benchmarks and large-scale language models demonstrate favorable trade-offs among accuracy, convergence, and communication, offering theoretical and practical insights for scalable, efficient distributed learning.", "authors": ["Kai Yi"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-10", "url": "https://arxiv.org/abs/2509.08233", "pdf_url": "https://arxiv.org/pdf/2509.08233v1", "arxiv_id": "2509.08233", "doi": "10.48550/arXiv.2509.08233", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2956}
{"id": "4199e48aa8d003042ed630a00e27de760174388a216eeebd01bde1a07611306e", "sources": ["arxiv", "semantic_scholar"], "title": "Accelerating Privacy-Preserving Federated Learning in Large-Scale LEO Satellite Systems", "abstract": "Large-scale low-Earth-orbit (LEO) satellite systems are increasingly valued for their ability to enable rapid and wide-area data exchange, thereby facilitating the collaborative training of artificial intelligence (AI) models across geographically distributed regions. Due to privacy concerns and regulatory constraints, raw data collected at remote clients cannot be centrally aggregated, posing a major obstacle to traditional AI training methods. Federated learning offers a privacy-preserving alternative by training local models on distributed devices and exchanging only model parameters. However, the dynamic topology and limited bandwidth of satellite systems will hinder timely parameter aggregation and distribution, resulting in prolonged training times. To address this challenge, we investigate the problem of scheduling federated learning over satellite networks and identify key bottlenecks that impact the overall duration of each training round. We propose a discrete temporal graph-based on-demand scheduling framework that dynamically allocates communication resources to accelerate federated learning. Simulation results demonstrate that the proposed approach achieves significant performance gains over traditional statistical multiplexing-based model exchange strategies, reducing overall round times by 14.20% to 41.48%. Moreover, the acceleration effect becomes more pronounced for larger models and higher numbers of clients, highlighting the scalability of the proposed approach.", "authors": ["Binquan Guo", "Junteng Cao", "Marie Siew", "Binbin Chen", "Tony Q. S. Quek", "Zhu Han"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-05", "url": "https://arxiv.org/abs/2509.12222", "pdf_url": "https://arxiv.org/pdf/2509.12222v1", "arxiv_id": "2509.12222", "doi": "10.1109/Trustcom66490.2025.00195", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Trust, Security and Privacy in Computing and Communications", "quality_score": 0.2899}
{"id": "e82e4cd9050b08ad6eafc0157efa1f341e635aafbbb866bf692133c03d83edba", "sources": ["arxiv", "semantic_scholar"], "title": "FedQuad: Federated Stochastic Quadruplet Learning to Mitigate Data Heterogeneity", "abstract": "Federated Learning (FL) provides decentralised model training, which effectively tackles problems such as distributed data and privacy preservation. However, the generalisation of global models frequently faces challenges from data heterogeneity among clients. This challenge becomes even more pronounced when datasets are limited in size and class imbalance. To address data heterogeneity, we propose a novel method, \\textit{FedQuad}, that explicitly optimises smaller intra-class variance and larger inter-class variance across clients, thereby decreasing the negative impact of model aggregation on the global model over client representations. Our approach minimises the distance between similar pairs while maximising the distance between negative pairs, effectively disentangling client data in the shared feature space. We evaluate our method on the CIFAR-10 and CIFAR-100 datasets under various data distributions and with many clients, demonstrating superior performance compared to existing approaches. Furthermore, we provide a detailed analysis of metric learning-based strategies within both supervised and federated learning paradigms, highlighting their efficacy in addressing representational learning challenges in federated settings.", "authors": ["Ozgu Goksu", "Nicolas Pugeault"], "categories": ["cs.LG", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-04", "url": "https://arxiv.org/abs/2509.04107", "pdf_url": "https://arxiv.org/pdf/2509.04107v1", "arxiv_id": "2509.04107", "doi": "10.1109/FLTA67013.2025.11336470", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1837}
{"id": "6285fe94f88d8da77e212ef8aeab62e7004b48c91b1ca0e66fbd6987e11c9cb2", "sources": ["arxiv", "semantic_scholar"], "title": "AI-in-the-Loop: Privacy Preserving Real-Time Scam Detection and Conversational Scambaiting by Leveraging LLMs and Federated Learning", "abstract": "Scams exploiting real-time social engineering -- such as phishing, impersonation, and phone fraud -- remain a persistent and evolving threat across digital platforms. Existing defenses are largely reactive, offering limited protection during active interactions. We propose a privacy-preserving, AI-in-the-loop framework that proactively detects and disrupts scam conversations in real time. The system combines instruction-tuned artificial intelligence with a safety-aware utility function that balances engagement with harm minimization, and employs federated learning to enable continual model updates without raw data sharing. Experimental evaluations show that the system produces fluent and engaging responses (perplexity as low as 22.3, engagement $\\approx$0.80), while human studies confirm significant gains in realism, safety, and effectiveness over strong baselines. In federated settings, models trained with FedAvg sustain up to 30 rounds while preserving high engagement ($\\approx$0.80), strong relevance ($\\approx$0.74), and low PII leakage ($\\leq$0.0085). Even with differential privacy, novelty and safety remain stable, indicating that robust privacy can be achieved without sacrificing performance. The evaluation of guard models (LlamaGuard, LlamaGuard2/3, MD-Judge) shows a straightforward pattern: stricter moderation settings reduce the chance of exposing personal information, but they also limit how much the model engages in conversation. In contrast, more relaxed settings allow longer and richer interactions, which improve scam detection, but at the cost of higher privacy risk. To our knowledge, this is the first framework to unify real-time scam-baiting, federated privacy preservation, and calibrated safety moderation into a proactive defense paradigm.", "authors": ["Ismail Hossain", "Sai Puppala", "Md Jahangir Alam", "Sajedul Talukder"], "categories": ["cs.CR", "cs.AI", "cs.LG", "cs.SI"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-04", "url": "https://arxiv.org/abs/2509.05362", "pdf_url": "https://arxiv.org/pdf/2509.05362v4", "arxiv_id": "2509.05362", "doi": "10.48550/arXiv.2509.05362", "citation_count": 3, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Proceedings on Privacy Enhancing Technologies", "quality_score": 0.2888}
{"id": "925621ad2f75055c0e13214a0c6a4927b140bf9f4df4427650851e72e6d8d93f", "sources": ["arxiv", "semantic_scholar"], "title": "Gaming and Cooperation in Federated Learning: What Can Happen and How to Monitor It", "abstract": "The success of federated learning (FL) ultimately depends on how strategic participants behave under partial observability, yet most formulations still treat FL as a static optimization problem. We instead view FL deployments as governed strategic systems and develop an analytical framework that separates welfare-improving behavior from metric gaming. Within this framework, we introduce indices that quantify manipulability, the price of gaming, and the price of cooperation, and we use them to study how rules, information disclosure, evaluation metrics, and aggregator-switching policies reshape incentives and cooperation patterns. We derive threshold conditions for deterring harmful gaming while preserving benign cooperation, and for triggering auto-switch rules when early-warning indicators become critical. Building on these results, we construct a design toolkit including a governance checklist and a simple audit-budget allocation algorithm with a provable performance guarantee. Simulations across diverse stylized environments and a federated learning case study consistently match the qualitative and quantitative patterns predicted by our framework. Taken together, our results provide design principles and operational guidelines for reducing metric gaming while sustaining stable, high-welfare cooperation in FL platforms.", "authors": ["Dongseok Kim", "Hyoungsun Choi", "Mohamed Jismy Aashik Rasool", "Gisung Oh"], "categories": ["cs.LG", "cs.GT", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2025-09-02", "url": "https://arxiv.org/abs/2509.02391", "pdf_url": "https://arxiv.org/pdf/2509.02391v3", "arxiv_id": "2509.02391", "doi": "10.48550/arXiv.2509.02391", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Transactions on Machine Learning Research, 2026", "quality_score": 0.2865}
{"id": "1eb9ec1129e27198a4701e3ffc5c0c3f9c13726d4ce9d78241f5ab653e4acf68", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Distillation on Edge Devices: Efficient Client-Side Filtering for Non-IID Data", "abstract": "Federated distillation has emerged as a promising collaborative machine learning approach, offering enhanced privacy protection and reduced communication compared to traditional federated learning by exchanging model outputs (soft logits) rather than full model parameters. However, existing methods employ complex selective knowledge-sharing strategies that require clients to identify in-distribution proxy data through computationally expensive statistical density ratio estimators. Additionally, server-side filtering of ambiguous knowledge introduces latency to the process. To address these challenges, we propose a robust, resource-efficient EdgeFD method that reduces the complexity of the client-side density ratio estimation and removes the need for server-side filtering. EdgeFD introduces an efficient KMeans-based density ratio estimator for effectively filtering both in-distribution and out-of-distribution proxy data on clients, significantly improving the quality of knowledge sharing. We evaluate EdgeFD across diverse practical scenarios, including strong non-IID, weak non-IID, and IID data distributions on clients, without requiring a pre-trained teacher model on the server for knowledge distillation. Experimental results demonstrate that EdgeFD outperforms state-of-the-art methods, consistently achieving accuracy levels close to IID scenarios even under heterogeneous and challenging conditions. The significantly reduced computational overhead of the KMeans-based estimator is suitable for deployment on resource-constrained edge devices, thereby enhancing the scalability and real-world applicability of federated distillation. The code is available online for reproducibility.", "authors": ["Ahmed Mujtaba", "Gleb Radchenko", "Radu Prodan", "Marc Masana"], "categories": ["cs.LG", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-20", "url": "https://arxiv.org/abs/2508.14769", "pdf_url": "https://arxiv.org/pdf/2508.14769v2", "arxiv_id": "2508.14769", "doi": "10.1109/FLTA67013.2025.11336390", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": null, "quality_score": 0.3209}
{"id": "867c052a7cfa9324c42adff43a0a9109360bcce00368bd90a3c6ce14ee2bafaf", "sources": ["arxiv", "semantic_scholar"], "title": "Beyond Trade-offs: A Unified Framework for Privacy, Robustness, and Communication Efficiency in Federated Learning", "abstract": "We propose Fed-DPRoC, a novel federated learning framework designed to jointly provide differential privacy (DP), Byzantine robustness, and communication efficiency. Central to our approach is the concept of robust-compatible compression, which allows reducing the bi-directional communication overhead without undermining the robustness of the aggregation. We instantiate our framework as RobAJoL, which integrates the Johnson-Lindenstrauss (JL)-based compression mechanism with robust averaging for robustness. Our theoretical analysis establishes the compatibility of JL transform with robust averaging, ensuring that RobAJoL maintains robustness guarantees, satisfies DP, and substantially reduces communication overhead. We further present simulation results on CIFAR-10, Fashion MNIST, and FEMNIST, validating our theoretical claims. We compare RobAJoL with a state-of-the-art communication-efficient and robust FL scheme augmented with DP for a fair comparison, demonstrating that RobAJoL outperforms existing methods in terms of robustness and utility under different Byzantine attacks.", "authors": ["Yue Xia", "Tayyebeh Jahani-Nezhad", "Rawad Bitar"], "categories": ["cs.LG", "cs.DC", "cs.IT"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2025-08-18", "url": "https://arxiv.org/abs/2508.12978", "pdf_url": "https://arxiv.org/pdf/2508.12978v2", "arxiv_id": "2508.12978", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1714}
{"id": "d38e684d5ab8772d898f9e0d392c5a3178b423525c9bc13f806b22573054c0da", "sources": ["arxiv", "semantic_scholar"], "title": "Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clustering", "abstract": "Federated Learning (FL) enables collaborative model training across multiple clients without sharing private data. We consider FL scenarios wherein FL clients are subject to adversarial (Byzantine) attacks, while the FL server is trusted (honest) and has a trustworthy side dataset. This may correspond to, e.g., cases where the server possesses trusted data prior to federation, or to the presence of a trusted client that temporarily assumes the server role. Our approach requires only two honest participants, i.e., the server and one client, to function effectively, without prior knowledge of the number of malicious clients. Theoretical analysis demonstrates bounded optimality gaps even under strong Byzantine attacks. Experimental results show that our algorithm significantly outperforms standard and robust FL baselines such as Mean, Trimmed Mean, Median, Krum, and Multi-Krum under various attack strategies including label flipping, sign flipping, and Gaussian noise addition across MNIST, FMNIST, and CIFAR-10 benchmarks using the Flower framework.", "authors": ["Emmanouil Kritharakis", "Dusan Jakovetic", "Antonios Makris", "Konstantinos Tserpes"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-18", "url": "https://arxiv.org/abs/2508.12672", "pdf_url": "https://arxiv.org/pdf/2508.12672v4", "arxiv_id": "2508.12672", "doi": "10.48550/arXiv.2508.12672", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2693}
{"id": "c6e44aca833dd8d2cd9cf718539ef2f5ffddeef5e6489fb8802dd7a9a8922594", "sources": ["arxiv", "semantic_scholar"], "title": "Deciphering the Interplay between Attack and Protection Complexity in Privacy-Preserving Federated Learning", "abstract": "Federated learning (FL) offers a promising paradigm for collaborative model training while preserving data privacy. However, its susceptibility to gradient inversion attacks poses a significant challenge, necessitating robust privacy protection mechanisms. This paper introduces a novel theoretical framework to decipher the intricate interplay between attack and protection complexities in privacy-preserving FL. We formally define \"Attack Complexity\" as the minimum computational and data resources an adversary requires to reconstruct private data below a given error threshold, and \"Protection Complexity\" as the expected distortion introduced by privacy mechanisms. Leveraging Maximum Bayesian Privacy (MBP), we derive tight theoretical bounds for protection complexity, demonstrating its scaling with model dimensionality and privacy budget. Furthermore, we establish comprehensive bounds for attack complexity, revealing its dependence on privacy leakage, gradient distortion, model dimension, and the chosen privacy level. Our findings quantitatively illuminate the fundamental trade-offs between privacy guarantees, system utility, and the effort required for both attacking and defending. This framework provides critical insights for designing more secure and efficient federated learning systems.", "authors": ["Xiaojin Zhang", "Mingcong Xu", "Yiming Li", "Wei Chen", "Qiang Yang"], "categories": ["cs.CR", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-16", "url": "https://arxiv.org/abs/2508.11907", "pdf_url": "https://arxiv.org/pdf/2508.11907v1", "arxiv_id": "2508.11907", "doi": "10.48550/arXiv.2508.11907", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.267}
{"id": "4f6d307073e76b5509ed4c32199abfc68156d50cf4d3918f3d9b0535d3069996", "sources": ["arxiv", "semantic_scholar"], "title": "Blockchain-Enabled Federated Learning", "abstract": "Blockchain-enabled federated learning (BCFL) addresses fundamental challenges of trust, privacy, and coordination in collaborative AI systems. This chapter provides comprehensive architectural analysis of BCFL systems through a systematic four-dimensional taxonomy examining coordination structures, consensus mechanisms, storage architectures, and trust models. We analyze design patterns from blockchain-verified centralized coordination to fully decentralized peer-to-peer networks, evaluating trade-offs in scalability, security, and performance. Through detailed examination of consensus mechanisms designed for federated learning contexts, including Proof of Quality and Proof of Federated Learning, we demonstrate how computational work can be repurposed from arbitrary cryptographic puzzles to productive machine learning tasks. The chapter addresses critical storage challenges by examining multi-tier architectures that balance blockchain's transaction constraints with neural networks' large parameter requirements while maintaining cryptographic integrity. A technical case study of the TrustMesh framework illustrates practical implementation considerations in BCFL systems through distributed image classification training, demonstrating effective collaborative learning across IoT devices with highly non-IID data distributions while maintaining complete transparency and fault tolerance. Analysis of real-world deployments across healthcare consortiums, financial services, and IoT security applications validates the practical viability of BCFL systems, achieving performance comparable to centralized approaches while providing enhanced security guarantees and enabling new models of trustless collaborative intelligence.", "authors": ["Murtaza Rangwala", "KR Venugopal", "Rajkumar Buyya"], "categories": ["cs.DC", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-08", "url": "https://arxiv.org/abs/2508.06406", "pdf_url": "https://arxiv.org/pdf/2508.06406v4", "arxiv_id": "2508.06406", "doi": "10.48550/arXiv.2508.06406", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2578}
{"id": "0bf4eae9cc28386bb4d68d4e5f771d1af702a99a35730f3710141d54a2ca641d", "sources": ["arxiv", "semantic_scholar"], "title": "FedMeNF: Privacy-Preserving Federated Meta-Learning for Neural Fields", "abstract": "Neural fields provide a memory-efficient representation of data, which can effectively handle diverse modalities and large-scale data. However, learning to map neural fields often requires large amounts of training data and computations, which can be limited to resource-constrained edge devices. One approach to tackle this limitation is to leverage Federated Meta-Learning (FML), but traditional FML approaches suffer from privacy leakage. To address these issues, we introduce a novel FML approach called FedMeNF. FedMeNF utilizes a new privacy-preserving loss function that regulates privacy leakage in the local meta-optimization. This enables the local meta-learner to optimize quickly and efficiently without retaining the client's private data. Our experiments demonstrate that FedMeNF achieves fast optimization speed and robust reconstruction performance, even with few-shot or non-IID data across diverse data modalities, while preserving client data privacy.", "authors": ["Junhyeog Yun", "Minui Hong", "Gunhee Kim"], "categories": ["cs.LG", "cs.AI", "cs.CV", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-08", "url": "https://arxiv.org/abs/2508.06301", "pdf_url": "https://arxiv.org/pdf/2508.06301v1", "arxiv_id": "2508.06301", "doi": "10.1109/ICCV51701.2025.00209", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Computer Vision", "quality_score": 0.2578}
{"id": "4d7ff1dd91c8a65fd17d4e06ed2e10346ab905331ce92c2c337d3444fb7c3ab2", "sources": ["arxiv", "semantic_scholar"], "title": "From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0", "abstract": "Federated Learning (FL) enables privacy-preserving collaborative learning, yet deployments increasingly show that privacy guarantees alone do not sustain trust in high-risk settings. As FL systems move toward agentic AI, large language model-enabled, and dynamically adaptive architectures, trustworthiness becomes a system-level problem shaped by autonomous decision-making, non-stationary environments, and multi-stakeholder governance. We argue for Trustworthy FL (TFL), treating trust as a continuously maintained operating condition rather than a static model property. Through the lens of Trust Report 2.0, we propose a requirement-driven taxonomy of challenges grounded in TAI and explicitly extended to account for control-plane decisions, agency, and system dynamics across the federated lifecycle. Building on this diagnosis, we introduce a coordination blueprint that structures cross-requirement trade-offs, decision justification, and governance alignment in TFL systems. To operationalize assurance, Trust Report 2.0 is instantiated as a lightweight, privacy-preserving artifact that surfaces decision-centric trust evidence without centralizing raw data. We illustrate applicability via healthcare as a stress-test domain, focusing on oncology FL under regulatory pressure and clinical risk.", "authors": ["Nuria Rodríguez-Barroso", "Mario García-Márquez", "M. Victoria Luzón", "Francisco Herrera"], "categories": ["cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-21", "url": "https://arxiv.org/abs/2507.15796", "pdf_url": "https://arxiv.org/pdf/2507.15796v2", "arxiv_id": "2507.15796", "doi": "10.1016/j.inffus.2026.104236", "citation_count": 3, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Information Fusion", "quality_score": 0.2372}
{"id": "d4d9d82cd9d8b821aebdf6a757133a18835c00f1f102d83a51161a0629822fcb", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning with Graph-Based Aggregation for Traffic Forecasting", "abstract": "In traffic prediction, the goal is to estimate traffic speed or flow in specific regions or road segments using historical data collected by devices deployed in each area. Each region or road segment can be viewed as an individual client that measures local traffic flow, making Federated Learning (FL) a suitable approach for collaboratively training models without sharing raw data. In centralized FL, a central server collects and aggregates model updates from multiple clients to build a shared model while preserving each client's data privacy. Standard FL methods, such as Federated Averaging (FedAvg), assume that clients are independent, which can limit performance in traffic prediction tasks where spatial relationships between clients are important. Federated Graph Learning methods can capture these dependencies during server-side aggregation, but they often introduce significant computational overhead. In this paper, we propose a lightweight graph-aware FL approach that blends the simplicity of FedAvg with key ideas from graph learning. Rather than training full models, our method applies basic neighbourhood aggregation principles to guide parameter updates, weighting client models based on graph connectivity. This approach captures spatial relationships effectively while remaining computationally efficient. We evaluate our method on two benchmark traffic datasets, METR-LA and PEMS-BAY, and show that it achieves competitive performance compared to standard baselines and recent graph-based federated learning techniques.", "authors": ["Audri Banik", "Glaucio Haroldo Silva de Carvalho", "Renata Dividino"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-13", "url": "https://arxiv.org/abs/2507.09805", "pdf_url": "https://arxiv.org/pdf/2507.09805v1", "arxiv_id": "2507.09805", "doi": "10.48550/arXiv.2507.09805", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.228}
{"id": "8640f735b154435ead086f9af4e2234ddfa45e1c08aefde2b2354636cc891f8e", "sources": ["arxiv", "semantic_scholar"], "title": "FedPhD: Federated Pruning with Hierarchical Learning of Diffusion Models", "abstract": "Federated Learning (FL), as a distributed learning paradigm, trains models over distributed clients' data. FL is particularly beneficial for distributed training of Diffusion Models (DMs), which are high-quality image generators that require diverse data. However, challenges such as high communication costs and data heterogeneity persist in training DMs similar to training Transformers and Convolutional Neural Networks. Limited research has addressed these issues in FL environments. To address this gap and challenges, we introduce a novel approach, FedPhD, designed to efficiently train DMs in FL environments. FedPhD leverages Hierarchical FL with homogeneity-aware model aggregation and selection policy to tackle data heterogeneity while reducing communication costs. The distributed structured pruning of FedPhD enhances computational efficiency and reduces model storage requirements in clients. Our experiments across multiple datasets demonstrate that FedPhD achieves high model performance regarding Fréchet Inception Distance (FID) scores while reducing communication costs by up to $88\\%$. FedPhD outperforms baseline methods achieving at least a $34\\%$ improvement in FID, while utilizing only $56\\%$ of the total computation and communication resources.", "authors": ["Qianyu Long", "Qiyuan Wang", "Christos Anagnostopoulos", "Daning Bi"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-08", "url": "https://arxiv.org/abs/2507.06449", "pdf_url": "https://arxiv.org/pdf/2507.06449v1", "arxiv_id": "2507.06449", "doi": "10.48550/arXiv.2507.06449", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2223}
{"id": "5b2b640cbe243336da66d7f9d765fe0f7c682d3c3b411089b1e02d5971aedacc", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Quantized Federated Learning with Diverse Precision", "abstract": "Federated learning (FL) has emerged as a promising paradigm for distributed machine learning, enabling collaborative training of a global model across multiple local devices without requiring them to share raw data. Despite its advancements, FL is limited by factors such as: (i) privacy risks arising from the unprotected transmission of local model updates to the fusion center (FC) and (ii) decreased learning utility caused by heterogeneity in model quantization resolution across participating devices. Prior work typically addresses only one of these challenges because maintaining learning utility under both privacy risks and quantization heterogeneity is a non-trivial task. In this paper, our aim is therefore to improve the learning utility of a privacy-preserving FL that allows clusters of devices with different quantization resolutions to participate in each FL round. Specifically, we introduce a novel stochastic quantizer (SQ) that is designed to simultaneously achieve differential privacy (DP) and minimum quantization error. Notably, the proposed SQ guarantees bounded distortion, unlike other DP approaches. To address quantization heterogeneity, we introduce a cluster size optimization technique combined with a linear fusion approach to enhance model aggregation accuracy. Numerical simulations validate the benefits of our approach in terms of privacy protection and learning utility compared to the conventional LaplaceSQ-FL algorithm.", "authors": ["Dang Qua Nguyen", "Morteza Hashemi", "Erik Perrins", "Sergiy A. Vorobyov", "David J. Love", "Taejoon Kim"], "categories": ["cs.LG", "eess.SP"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2025-07-01", "url": "https://arxiv.org/abs/2507.00920", "pdf_url": "https://arxiv.org/pdf/2507.00920v2", "arxiv_id": "2507.00920", "doi": "10.48550/arXiv.2507.00920", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2143}
{"id": "3647235fe3c2688efb7cf794f04813db536da0b89d36060c39c7e9ec8daa0555", "sources": ["arxiv", "semantic_scholar"], "title": "PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction", "abstract": "Reconstructing high-quality images from low-resolution inputs using Residual Dense Spatial Networks (RDSNs) is crucial yet challenging. It is even more challenging in centralized training where multiple collaborating parties are involved, as it poses significant privacy risks, including data leakage and inference attacks, as well as high computational and communication costs. We propose a novel Privacy-Preserving Federated Learning-based RDSN (PPFL-RDSN) framework specifically tailored for encrypted lossy image reconstruction. PPFL-RDSN integrates Federated Learning (FL), local differential privacy, and robust model watermarking techniques to ensure that data remains secure on local clients/devices, safeguards privacy-sensitive information, and maintains model authenticity without revealing underlying data. Empirical evaluations show that PPFL-RDSN achieves comparable performance to the state-of-the-art centralized methods while reducing computational burdens, and effectively mitigates security and privacy vulnerabilities, making it a practical solution for secure and privacy-preserving collaborative computer vision applications.", "authors": ["Peilin He", "James Joshi"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-30", "url": "https://arxiv.org/abs/2507.00230", "pdf_url": "https://arxiv.org/pdf/2507.00230v3", "arxiv_id": "2507.00230", "doi": "10.1109/TPS-ISA67132.2025.00013", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Trust, Privacy and Security in Intelligent Systems and Applications", "quality_score": 0.2131}
{"id": "cf405b471f6fc965057f56990afe2b71fd537d39924000856dc591712f7db23c", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Federated Learning Scheme with Mitigating Model Poisoning Attacks: Vulnerabilities and Countermeasures", "abstract": "The privacy-preserving federated learning schemes based on the setting of two honest-but-curious and non-colluding servers offer promising solutions in terms of security and efficiency. However, our investigation reveals that these schemes still suffer from privacy leakage when considering model poisoning attacks from malicious users. Specifically, we demonstrate that the privacy-preserving computation process for defending against model poisoning attacks inadvertently leaks privacy to one of the honest-but-curious servers, enabling it to access users' gradients in plaintext. To address both privacy leakage and model poisoning attacks, we propose an enhanced privacy-preserving and Byzantine-robust federated learning (PBFL) scheme, comprising three components: (1) a two-trapdoor fully homomorphic encryption (FHE) scheme to bolster users' privacy protection; (2) a novel secure normalization judgment method to preemptively thwart gradient poisoning; and (3) an innovative secure cosine similarity measurement method for detecting model poisoning attacks without compromising data privacy. Our scheme guarantees privacy preservation and resilience against model poisoning attacks, even in scenarios with heterogeneous, non-IID (Independently and Identically Distributed) datasets. Theoretical analyses substantiate the security and efficiency of our scheme, and extensive experiments corroborate the efficacy of our private attacks. Furthermore, the experimental results demonstrate that our scheme accelerates training speed while reducing communication overhead compared to the state-of-the-art PBFL schemes.", "authors": ["Jiahui Wu", "Fucai Luo", "Tiecheng Sun", "Haiyan Wang", "Weizhe Zhang"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-30", "url": "https://arxiv.org/abs/2506.23622", "pdf_url": "https://arxiv.org/pdf/2506.23622v2", "arxiv_id": "2506.23622", "doi": "10.1109/TDSC.2025.3617070", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Dependable and Secure Computing", "quality_score": 0.2131}
{"id": "d627c7ca37ba34f90c9a0f348aacab5831e1e03bf382e25919719546b59be663", "sources": ["arxiv", "semantic_scholar"], "title": "FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learning", "abstract": "Black-Box Discrete Prompt Learning is a prompt-tuning method that optimizes discrete prompts without accessing model parameters or gradients, making the prompt tuning on a cloud-based Large Language Model (LLM) feasible. Adapting federated learning to BDPL could further enhance prompt tuning performance by leveraging data from diverse sources. However, all previous research on federated black-box prompt tuning had neglected the substantial query cost associated with the cloud-based LLM service. To address this gap, we conducted a theoretical analysis of query efficiency within the context of federated black-box prompt tuning. Our findings revealed that degrading FedAvg to activate only one client per round, a strategy we called \\textit{FedOne}, enabled optimal query efficiency in federated black-box prompt learning. Building on this insight, we proposed the FedOne framework, a federated black-box discrete prompt learning method designed to maximize query efficiency when interacting with cloud-based LLMs. We conducted numerical experiments on various aspects of our framework, demonstrating a significant improvement in query efficiency, which aligns with our theoretical results.", "authors": ["Ganyu Wang", "Jinjie Fang", "Maxwell J. Yin", "Bin Gu", "Xi Chen", "Boyu Wang", "Yi Chang", "Charles Ling"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-17", "url": "https://arxiv.org/abs/2506.14929", "pdf_url": "https://arxiv.org/pdf/2506.14929v2", "arxiv_id": "2506.14929", "doi": "10.48550/arXiv.2506.14929", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Machine Learning", "quality_score": 0.1982}
{"id": "2dcd9acc56737307694eecd5c39ee21e011b1a7bf008ec74838b0463e0823496", "sources": ["arxiv", "semantic_scholar"], "title": "Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning", "abstract": "Personalized federated learning (PFL), e.g., the renowned Ditto, strikes a balance between personalization and generalization by conducting federated learning (FL) to guide personalized learning (PL). While FL is unaffected by personalized model training, in Ditto, PL depends on the outcome of the FL. However, the clients' concern about their privacy and consequent perturbation of their local models can affect the convergence and (performance) fairness of PL. This paper presents PFL, called DP-Ditto, which is a non-trivial extension of Ditto under the protection of differential privacy (DP), and analyzes the trade-off among its privacy guarantee, model convergence, and performance distribution fairness. We also analyze the convergence upper bound of the personalized models under DP-Ditto and derive the optimal number of global aggregations given a privacy budget. Further, we analyze the performance fairness of the personalized models, and reveal the feasibility of optimizing DP-Ditto jointly for convergence and fairness. Experiments validate our analysis and demonstrate that DP-Ditto can surpass the DP-perturbed versions of the state-of-the-art PFL models, such as FedAMP, pFedMe, APPLE, and FedALA, by over 32.71% in fairness and 9.66% in accuracy.", "authors": ["Xiyu Zhao", "Qimei Cui", "Weicai Li", "Wei Ni", "Ekram Hossain", "Quan Z. Sheng", "Xiaofeng Tao", "Ping Zhang"], "categories": ["cs.LG", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-17", "url": "https://arxiv.org/abs/2506.14251", "pdf_url": "https://arxiv.org/pdf/2506.14251v1", "arxiv_id": "2506.14251", "doi": "10.1109/TMLCN.2025.3528901", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Machine Learning in Communications and Networking", "quality_score": 0.1982}
{"id": "9264a2f270ad9e2aa25b43555eee6a52d8e1c19970dfa4230a26738d8af2627a", "sources": ["arxiv", "semantic_scholar"], "title": "VFEFL: Privacy-Preserving Federated Learning against Malicious Clients via Verifiable Functional Encryption", "abstract": "Federated learning is a promising distributed learning paradigm that enables collaborative model training without exposing local client data, thereby protecting data privacy. However, it also brings new threats and challenges. The advancement of model inversion attacks has rendered the plaintext transmission of local models insecure, while the distributed nature of federated learning makes it particularly vulnerable to attacks raised by malicious clients. To protect data privacy and prevent malicious client attacks, this paper proposes a privacy-preserving Federated Learning framework based on Verifiable Functional Encryption (VFEFL), without a non-colluding dual-server assumption or additional trusted third-party. Specifically, we propose a novel Cross-Ciphertext Decentralized Verifiable Functional Encryption (CC-DVFE) scheme that enables the verification of specific relationships over multi-dimensional ciphertexts. This scheme is formally treated, in terms of definition, security model and security proof. Furthermore, based on the proposed CC-DVFE scheme, we design a privacy-preserving federated learning framework that incorporates a novel robust aggregation rule to detect malicious clients, enabling the effective training of high-accuracy models under adversarial settings. Finally, we provide the formal analysis and empirical evaluation of VFEFL. The results demonstrate that our approach achieves the desired privacy protection, robustness, verifiability and fidelity, while eliminating the reliance on non-colluding dual-server assumption or trusted third parties required by most existing methods.", "authors": ["Nina Cai", "Jinguang Han", "Weizhi Meng"], "categories": ["cs.CR", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-15", "url": "https://arxiv.org/abs/2506.12846", "pdf_url": "https://arxiv.org/pdf/2506.12846v10", "arxiv_id": "2506.12846", "doi": "10.48550/arXiv.2506.12846", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal of Information Security and Applications", "quality_score": 0.1959}
{"id": "54f959aa2df2bf80b082a9881966db6985c6f9f44a7541d547627915efc24526", "sources": ["arxiv", "semantic_scholar"], "title": "TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems", "abstract": "Federated Learning has emerged as a privacy-oriented alternative to centralized Machine Learning, enabling collaborative model training without direct data sharing. While extensively studied for neural networks, the security and privacy implications of tree-based models remain underexplored. This work introduces TimberStrike, an optimization-based dataset reconstruction attack targeting horizontally federated tree-based models. Our attack, carried out by a single client, exploits the discrete nature of decision trees by using split values and decision paths to infer sensitive training data from other clients. We evaluate TimberStrike on State-of-the-Art federated gradient boosting implementations across multiple frameworks, including Flower, NVFlare, and FedTree, demonstrating their vulnerability to privacy breaches. On a publicly available stroke prediction dataset, TimberStrike consistently reconstructs between 73.05% and 95.63% of the target dataset across all implementations. We further analyze Differential Privacy, showing that while it partially mitigates the attack, it also significantly degrades model performance. Our findings highlight the need for privacy-preserving mechanisms specifically designed for tree-based Federated Learning systems, and we provide preliminary insights into their design.", "authors": ["Marco Di Gennaro", "Giovanni De Lucia", "Stefano Longari", "Stefano Zanero", "Michele Carminati"], "categories": ["cs.CR", "cs.DC", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-09", "url": "https://arxiv.org/abs/2506.07605", "pdf_url": "https://arxiv.org/pdf/2506.07605v3", "arxiv_id": "2506.07605", "doi": "10.56553/popets-2025-0145", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Proceedings on Privacy Enhancing Technologies", "quality_score": 0.1891}
{"id": "ee2b08b75c51e46f4b7635e1253d85ca1a18c521036a319853dd7eaeed36be67", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning on Stochastic Neural Networks", "abstract": "Federated learning is a machine learning paradigm that leverages edge computing on client devices to optimize models while maintaining user privacy by ensuring that local data remains on the device. However, since all data is collected by clients, federated learning is susceptible to latent noise in local datasets. Factors such as limited measurement capabilities or human errors may introduce inaccuracies in client data. To address this challenge, we propose the use of a stochastic neural network as the local model within the federated learning framework. Stochastic neural networks not only facilitate the estimation of the true underlying states of the data but also enable the quantification of latent noise. We refer to our federated learning approach, which incorporates stochastic neural networks as local models, as Federated stochastic neural networks. We will present numerical experiments demonstrating the performance and effectiveness of our method, particularly in handling non-independent and identically distributed data.", "authors": ["Jingqiao Tang", "Ryan Bausback", "Feng Bao", "Richard Archibald"], "categories": ["cs.LG", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-09", "url": "https://arxiv.org/abs/2506.08169", "pdf_url": "https://arxiv.org/pdf/2506.08169v1", "arxiv_id": "2506.08169", "doi": "10.48550/arXiv.2506.08169", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1891}
{"id": "7dab684982c206b0f79f097716936584edc9baca1ea5fe73687ffdf4d35b324e", "sources": ["arxiv", "semantic_scholar"], "title": "Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review", "abstract": "Federated Learning (FL) is a learning mechanism that falls under the distributed training umbrella, which collaboratively trains a shared global model without disclosing the raw data from different clients. This paper presents an extensive survey on the impact of partial client participation in federated learning. While much of the existing research focuses on addressing issues such as generalization, robustness, and fairness caused by data heterogeneity under the assumption of full client participation, limited attention has been given to the practical and theoretical challenges arising from partial client participation, which is common in real-world scenarios. This survey provides an in-depth review of existing FL methods designed to cope with partial client participation. We offer a comprehensive analysis supported by theoretical insights and empirical findings, along with a structured categorization of these methods, highlighting their respective advantages and disadvantages.", "authors": ["Mrinmay Sen", "Shruti Aparna", "Rohit Agarwal", "Chalavadi Krishna Mohan"], "categories": ["cs.LG", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-03", "url": "https://arxiv.org/abs/2506.02887", "pdf_url": "https://arxiv.org/pdf/2506.02887v2", "arxiv_id": "2506.02887", "doi": "10.48550/arXiv.2506.02887", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1822}
{"id": "98f0dc552bb621181e5917fe6098c57367a302900e8ef284600dbff7b7c69c3c", "sources": ["arxiv", "semantic_scholar"], "title": "Blockchain-Enabled Privacy-Preserving Second-Order Federated Edge Learning in Personalized Healthcare", "abstract": "Federated learning (FL) is increasingly recognised for addressing security and privacy concerns in traditional cloud-centric machine learning (ML), particularly within personalised health monitoring such as wearable devices. By enabling global model training through localised policies, FL allows resource-constrained wearables to operate independently. However, conventional first-order FL approaches face several challenges in personalised model training due to the heterogeneous non-independent and identically distributed (non-iid) data by each individual's unique physiology and usage patterns. Recently, second-order FL approaches maintain the stability and consistency of non-iid datasets while improving personalised model training. This study proposes and develops a verifiable and auditable optimised second-order FL framework BFEL (blockchain enhanced federated edge learning) based on optimised FedCurv for personalised healthcare systems. FedCurv incorporates information about the importance of each parameter to each client's task (through fisher information matrix) which helps to preserve client-specific knowledge and reduce model drift during aggregation. Moreover, it minimizes communication rounds required to achieve a target precision convergence for each client device while effectively managing personalised training on non-iid and heterogeneous data. The incorporation of ethereum-based model aggregation ensures trust, verifiability, and auditability while public key encryption enhances privacy and security. Experimental results of federated CNNs and MLPs utilizing mnist, cifar-10, and PathMnist demonstrate framework's high efficiency, scalability, suitability for edge deployment on wearables, and significant reduction in communication cost.", "authors": ["Anum Nawaz", "Muhammad Irfan", "Xianjia Yu", "Hamad Aldawsari", "Rayan Hamza Alsisi", "Zhuo Zou", "Tomi Westerlund"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2025-05-31", "url": "https://arxiv.org/abs/2506.00416", "pdf_url": "https://arxiv.org/pdf/2506.00416v2", "arxiv_id": "2506.00416", "doi": "10.1109/TCE.2025.3620115", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE transactions on consumer electronics", "quality_score": 0.1788}
{"id": "b91142b4521d2f8fcdecca5f4cb87da9a2e0d474809ab83e549268b20a23c417", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Foundation Language Model Post-Training Should Focus on Open-Source Models", "abstract": "Post-training of foundation language models has emerged as a promising research domain in federated learning (FL) with the goal to enable privacy-preserving model improvements and adaptations to user's downstream tasks. Recent advances in this area adopt centralized post-training approaches that build upon black-box foundation language models where there is no access to model weights and architecture details. Although the use of black-box models has been successful in centralized post-training, their blind replication in FL raises several concerns. Our opinion is that using black-box models in FL contradicts the core principles of federation such as data privacy and autonomy. In this paper, we critically analyze the usage of black-box models in federated post-training, and provide a detailed account of various aspects of openness and their implications for FL.", "authors": ["Nikita Agrawal", "Ruben Mayer"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-29", "url": "https://arxiv.org/abs/2505.23593", "pdf_url": "https://arxiv.org/pdf/2505.23593v4", "arxiv_id": "2505.23593", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1123}
{"id": "5d3b007b50a090cf3e4bf22b23a18827a0142517a0617bb6f27cb51168f75022", "sources": ["arxiv", "semantic_scholar"], "title": "CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning", "abstract": "Privacy-Preserving Federated Learning (PPFL) is a decentralized machine learning approach where multiple clients train a model collaboratively. PPFL preserves the privacy and security of a client's data without exchanging it. However, ensuring that data at each client is of high quality and ready for federated learning (FL) is a challenge due to restricted data access. In this paper, we introduce CADRE (Customizable Assurance of Data Readiness) for federated learning (FL), a novel framework that allows users to define custom data readiness (DR) metrics, rules, and remedies tailored to specific FL tasks. CADRE generates comprehensive DR reports based on the user-defined metrics, rules, and remedies to ensure datasets are prepared for FL while preserving privacy. We demonstrate a practical application of CADRE by integrating it into an existing PPFL framework. We conducted experiments across six datasets and addressed seven different DR issues. The results illustrate the versatility and effectiveness of CADRE in ensuring DR across various dimensions, including data quality, privacy, and fairness. This approach enhances the performance and reliability of FL models as well as utilizes valuable resources.", "authors": ["Kaveen Hiniduma", "Zilinghan Li", "Aditya Sinha", "Ravi Madduri", "Suren Byna"], "categories": ["cs.CR", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-28", "url": "https://arxiv.org/abs/2505.23849", "pdf_url": "https://arxiv.org/pdf/2505.23849v2", "arxiv_id": "2505.23849", "doi": "10.1109/eScience65000.2025.00023", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "eScience", "quality_score": 0.1753}
{"id": "075ecc2a1d651b22084db107e83e73e109d9f7bd3fda709621568a4536a66fa7", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Deep Reinforcement Learning for Privacy-Preserving Robotic-Assisted Surgery", "abstract": "The integration of Reinforcement Learning (RL) into robotic-assisted surgery (RAS) holds significant promise for advancing surgical precision, adaptability, and autonomous decision-making. However, the development of robust RL models in clinical settings is hindered by key challenges, including stringent patient data privacy regulations, limited access to diverse surgical datasets, and high procedural variability. To address these limitations, this paper presents a Federated Deep Reinforcement Learning (FDRL) framework that enables decentralized training of RL models across multiple healthcare institutions without exposing sensitive patient information. A central innovation of the proposed framework is its dynamic policy adaptation mechanism, which allows surgical robots to select and tailor patient-specific policies in real-time, thereby ensuring personalized and Optimised interventions. To uphold rigorous privacy standards while facilitating collaborative learning, the FDRL framework incorporates secure aggregation, differential privacy, and homomorphic encryption techniques. Experimental results demonstrate a 60\\% reduction in privacy leakage compared to conventional methods, with surgical precision maintained within a 1.5\\% margin of a centralized baseline. This work establishes a foundational approach for adaptive, secure, and patient-centric AI-driven surgical robotics, offering a pathway toward clinical translation and scalable deployment across diverse healthcare environments.", "authors": ["Sana Hafeez", "Sundas Rafat Mulkana", "Muhammad Ali Imran", "Michele Sevegnani"], "categories": ["cs.RO"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-17", "url": "https://arxiv.org/abs/2505.12153", "pdf_url": "https://arxiv.org/pdf/2505.12153v2", "arxiv_id": "2505.12153", "doi": "10.1109/ICDCSW63273.2025.00128", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Distributed Computing Systems Workshops", "quality_score": 0.1627}
{"id": "9dd2d704130a7d8989e9f9ba2a319dbe18547ddfcba196eb7e9d643d9101c1fe", "sources": ["arxiv", "semantic_scholar"], "title": "FedTDP: A Privacy-Preserving and Unified Framework for Trajectory Data Preparation via Federated Learning", "abstract": "Trajectory data, which capture the movement patterns of people and vehicles over time and space, are crucial for applications like traffic optimization and urban planning. However, issues such as noise and incompleteness often compromise data quality, leading to inaccurate trajectory analyses and limiting the potential of these applications. While Trajectory Data Preparation (TDP) can enhance data quality, existing methods suffer from two key limitations: (i) they do not address data privacy concerns, particularly in federated settings where trajectory data sharing is prohibited, and (ii) they typically design task-specific models that lack generalizability across diverse TDP scenarios. To overcome these challenges, we propose FedTDP, a privacy-preserving and unified framework that leverages the capabilities of Large Language Models (LLMs) for TDP in federated environments. Specifically, we: (i) design a trajectory privacy autoencoder to secure data transmission and protect privacy, (ii) introduce a trajectory knowledge enhancer to improve model learning of TDP-related knowledge, enabling the development of TDP-oriented LLMs, and (iii) propose federated parallel optimization to enhance training efficiency by reducing data transmission and enabling parallel model training. Experiments on 6 real datasets and 10 mainstream TDP tasks demonstrate that FedTDP consistently outperforms 13 state-of-the-art baselines.", "authors": ["Zhihao Zeng", "Ziquan Fang", "Wei Shao", "Lu Chen", "Yunjun Gao"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-08", "url": "https://arxiv.org/abs/2505.05155", "pdf_url": "https://arxiv.org/pdf/2505.05155v1", "arxiv_id": "2505.05155", "doi": "10.48550/arXiv.2505.05155", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1524}
{"id": "0911369fb3f2d36c0720bd15ffe7f0d763d53b26cca544f2095c3c24087c2a51", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning", "abstract": "The widespread adoption of Artificial Intelligence (AI) has been driven by significant advances in intelligent system research. However, this progress has raised concerns about data privacy, leading to a growing awareness of the need for privacy-preserving AI. In response, there has been a seismic shift in interest towards the leading paradigm for training Machine Learning (ML) models on decentralized data silos while maintaining data privacy, Federated Learning (FL). This research paper presents a comprehensive performance analysis of a cutting-edge approach to personalize ML model while preserving privacy achieved through Privacy Preserving Machine Learning with the innovative framework of Federated Personalized Learning (PPMLFPL). Regarding the increasing concerns about data privacy, this study evaluates the effectiveness of PPMLFPL addressing the critical balance between personalized model refinement and maintaining the confidentiality of individual user data. According to our analysis, Adaptive Personalized Cross-Silo Federated Learning with Differential Privacy (APPLE+DP) offering efficient execution whereas overall, the use of the Adaptive Personalized Cross-Silo Federated Learning with Homomorphic Encryption (APPLE+HE) algorithm for privacy-preserving machine learning tasks in federated personalized learning settings is strongly suggested. The results offer valuable insights creating it a promising scope for future advancements in the field of privacy-conscious data-driven technologies.", "authors": ["Md. Tanzib Hosain", "Asif Zaman", "Md. Shahriar Sajid", "Shadman Sakeeb Khan", "Shanjida Akter"], "categories": ["cs.LG", "cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-03", "url": "https://arxiv.org/abs/2505.01788", "pdf_url": "https://arxiv.org/pdf/2505.01788v1", "arxiv_id": "2505.01788", "doi": "10.1109/ICDABI60145.2023.10629638", "citation_count": 14, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.301}
{"id": "f0d7e31e7b24ab6e6409086fceda8c8b26f3e1577751b8e5cde1f86aaea74b0a", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation", "abstract": "Retrieval-Augmented Generation (RAG) has recently emerged as a promising solution for enhancing the accuracy and credibility of Large Language Models (LLMs), particularly in Question & Answer tasks. This is achieved by incorporating proprietary and private data from integrated databases. However, private RAG systems face significant challenges due to the scarcity of private domain data and critical data privacy issues. These obstacles impede the deployment of private RAG systems, as developing privacy-preserving RAG systems requires a delicate balance between data security and data availability. To address these challenges, we regard federated learning (FL) as a highly promising technology for privacy-preserving RAG services. We propose a novel framework called Federated Retrieval-Augmented Generation (FedE4RAG). This framework facilitates collaborative training of client-side RAG retrieval models. The parameters of these models are aggregated and distributed on a central-server, ensuring data privacy without direct sharing of raw data. In FedE4RAG, knowledge distillation is employed for communication between the server and client models. This technique improves the generalization of local RAG retrievers during the federated learning process. Additionally, we apply homomorphic encryption within federated learning to safeguard model parameters and mitigate concerns related to data leakage. Extensive experiments conducted on the real-world dataset have validated the effectiveness of FedE4RAG. The results demonstrate that our proposed framework can markedly enhance the performance of private RAG systems while maintaining robust data privacy protection.", "authors": ["Qianren Mao", "Qili Zhang", "Hanwen Hao", "Zhentao Han", "Runhua Xu", "Weifeng Jiang", "Qi Hu", "Zhijun Chen", "Tyler Zhou", "Bo Li", "Yangqiu Song", "Jin Dong", "Jianxin Li", "Philip S. Yu"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-27", "url": "https://arxiv.org/abs/2504.19101", "pdf_url": "https://arxiv.org/pdf/2504.19101v1", "arxiv_id": "2504.19101", "doi": "10.48550/arXiv.2504.19101", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2113}
{"id": "fc9c2e7e2167c5325ad8d8b193898d0e7b835e06a84030bda7aaae93bcb625df", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity", "abstract": "The rapid expansion of distributed photovoltaic (PV) installations worldwide, many being behind-the-meter systems, has significantly challenged energy management and grid operations, as unobservable PV generation further complicates the supply-demand balance. Therefore, estimating this generation from net load, known as PV disaggregation, is critical. Given privacy concerns and the need for large training datasets, federated learning becomes a promising approach, but statistical heterogeneity, arising from geographical and behavioral variations among prosumers, poses new challenges to PV disaggregation. To overcome these challenges, a privacy-preserving distributed PV disaggregation framework is proposed using Personalized Federated Learning (PFL). The proposed method employs a two-level framework that combines local and global modeling. At the local level, a transformer-based PV disaggregation model is designed to generate solar irradiance embeddings for representing local PV conditions. A novel adaptive local aggregation mechanism is adopted to mitigate the impact of statistical heterogeneity on the local model, extracting a portion of global information that benefits the local model. At the global level, a central server aggregates information uploaded from multiple data centers, preserving privacy while enabling cross-center knowledge sharing. Experiments on real-world data demonstrate the effectiveness of this proposed framework, showing improved accuracy and robustness compared to benchmark methods.", "authors": ["Xiaolu Chen", "Chenghao Huang", "Yanru Zhang", "Hao Wang"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-25", "url": "https://arxiv.org/abs/2504.18078", "pdf_url": "https://arxiv.org/pdf/2504.18078v2", "arxiv_id": "2504.18078", "doi": "10.1109/TIM.2025.3569908", "citation_count": 3, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Instrumentation and Measurement", "quality_score": 0.1505}
{"id": "b1c5aee83d494dad11aa404b569d3175f83674162f3aee23f4dc5cd15374eade", "sources": ["arxiv", "semantic_scholar"], "title": "FedDiverse: Tackling Data Heterogeneity in Federated Learning with Diversity-Driven Client Selection", "abstract": "Federated Learning (FL) enables decentralized training of machine learning models on distributed data while preserving privacy. However, in real-world FL settings, client data is often non-identically distributed and imbalanced, resulting in statistical data heterogeneity which impacts the generalization capabilities of the server's model across clients, slows convergence and reduces performance. In this paper, we address this challenge by proposing first a characterization of statistical data heterogeneity by means of 6 metrics of global and client attribute imbalance, class imbalance, and spurious correlations. Next, we create and share 7 computer vision datasets for binary and multiclass image classification tasks in Federated Learning that cover a broad range of statistical data heterogeneity and hence simulate real-world situations. Finally, we propose FEDDIVERSE, a novel client selection algorithm in FL which is designed to manage and leverage data heterogeneity across clients by promoting collaboration between clients with complementary data distributions. Experiments on the seven proposed FL datasets demonstrate FEDDIVERSE's effectiveness in enhancing the performance and robustness of a variety of FL methods while having low communication and computational overhead.", "authors": ["Gergely D. Németh", "Eros Fanì", "Yeat Jeng Ng", "Barbara Caputo", "Miguel Ángel Lozano", "Nuria Oliver", "Novi Quadrianto"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-15", "url": "https://arxiv.org/abs/2504.11216", "pdf_url": "https://arxiv.org/pdf/2504.11216v2", "arxiv_id": "2504.11216", "doi": "10.1109/FLTA67013.2025.11336421", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0802}
{"id": "1fd3f5d5f91d7e271dce8185da3e01481109dbfa812c594ead1f7c08b06c4160", "sources": ["arxiv", "semantic_scholar"], "title": "PPFPL: Cross-silo Privacy-preserving Federated Prototype Learning Against Data Poisoning Attacks", "abstract": "Privacy-Preserving Federated Learning (PPFL) enables multiple clients to collaboratively train models by submitting secreted model updates. Nonetheless, PPFL is vulnerable to data poisoning attacks due to its distributed training paradigm in cross-silo scenarios. Existing solutions have struggled to improve the performance of PPFL under poisoned Non-Independent and Identically Distributed (Non-IID) data. To address the issues, this paper proposes a privacy-preserving federated prototype learning framework, named PPFPL, which enhances the cross-silo FL performance against poisoned Non-IID data while protecting client privacy. Specifically, we adopt prototypes as client-submitted model updates to eliminate the impact of poisoned data distributions. In addition, we design a secure aggregation protocol utilizing homomorphic encryption to achieve Byzantine-robust aggregation on two servers, significantly reducing the impact of malicious clients. Theoretical analyses confirm the convergence and privacy of PPFPL. Experimental results on public datasets show that PPFPL effectively resists data poisoning attacks under Non-IID settings.", "authors": ["Hongliang Zhang", "Jiguo Yu", "Fenghua Xu", "Chunqiang Hu", "Yongzhao Zhang", "Xiaofen Wang", "Zhongyuan Yu", "Xiaosong Zhang"], "categories": ["cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-04", "url": "https://arxiv.org/abs/2504.03173", "pdf_url": "https://arxiv.org/pdf/2504.03173v5", "arxiv_id": "2504.03173", "doi": "10.1109/TAI.2025.3643391", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Artificial Intelligence", "quality_score": 0.1134}
{"id": "336a68a2deabc4deeeb4b0584f8fc39190a0eb771a30847dc7687f87bed84bd6", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration", "abstract": "This paper proposes a data privacy protection framework based on federated learning, which aims to realize effective cross-domain data collaboration under the premise of ensuring data privacy through distributed learning. Federated learning greatly reduces the risk of privacy breaches by training the model locally on each client and sharing only model parameters rather than raw data. The experiment verifies the high efficiency and privacy protection ability of federated learning under different data sources through the simulation of medical, financial, and user data. The results show that federated learning can not only maintain high model performance in a multi-domain data environment but also ensure effective protection of data privacy. The research in this paper provides a new technical path for cross-domain data collaboration and promotes the application of large-scale data analysis and machine learning while protecting privacy.", "authors": ["Yiwei Zhang", "Jie Liu", "Jiawei Wang", "Lu Dai", "Fan Guo", "Guohui Cai"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-31", "url": "https://arxiv.org/abs/2504.00282", "pdf_url": "https://arxiv.org/pdf/2504.00282v1", "arxiv_id": "2504.00282", "doi": "10.1109/ISBDAS64762.2025.11116917", "citation_count": 18, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3197}
{"id": "9233072d705b9f26815602f7a782e059ac45fbf91737256dc1fef55e60768267", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning", "abstract": "The increasing adoption of data-driven applications in education such as in learning analytics and AI in education has raised significant privacy and data protection concerns. While these challenges have been widely discussed in previous works, there are still limited practical solutions. Federated learning has recently been discoursed as a promising privacy-preserving technique, yet its application in education remains scarce. This paper presents an experimental evaluation of federated learning for educational data prediction, comparing its performance to traditional non-federated approaches. Our findings indicate that federated learning achieves comparable predictive accuracy. Furthermore, under adversarial attacks, federated learning demonstrates greater resilience compared to non-federated settings. We summarise that our results reinforce the value of federated learning as a potential approach for balancing predictive performance and privacy in educational contexts.", "authors": ["Mohammad Khalil", "Ronas Shakya", "Qinyi Liu"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-16", "url": "https://arxiv.org/abs/2503.13550", "pdf_url": "https://arxiv.org/pdf/2503.13550v1", "arxiv_id": "2503.13550", "doi": "10.1109/ICTCS65341.2025.10989403", "citation_count": 14, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Italian Conference on Theoretical Computer Science", "quality_score": 0.294}
{"id": "67f70bb94fa5f80ea6d450ad9da43fec594775e7237f7e52922acc02aefac570", "sources": ["arxiv", "semantic_scholar"], "title": "Research on Large Language Model Cross-Cloud Privacy Protection and Collaborative Training based on Federated Learning", "abstract": "The fast development of large language models (LLMs) and popularization of cloud computing have led to increasing concerns on privacy safeguarding and data security of cross-cloud model deployment and training as the key challenges. We present a new framework for addressing these issues along with enabling privacy preserving collaboration on training between distributed clouds based on federated learning. Our mechanism encompasses cutting-edge cryptographic primitives, dynamic model aggregation techniques, and cross-cloud data harmonization solutions to enhance security, efficiency, and scalability to the traditional federated learning paradigm. Furthermore, we proposed a hybrid aggregation scheme to mitigate the threat of Data Leakage and to optimize the aggregation of model updates, thus achieving substantial enhancement on the model effectiveness and stability. Experimental results demonstrate that the training efficiency, privacy protection, and model accuracy of the proposed model compare favorably to those of the traditional federated learning method.", "authors": ["Ze Yang", "Yihong Jin", "Yihan Zhang", "Juntian Liu", "Xinhe Xu"], "categories": ["cs.CR", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-15", "url": "https://arxiv.org/abs/2503.12226", "pdf_url": "https://arxiv.org/pdf/2503.12226v1", "arxiv_id": "2503.12226", "doi": "10.1109/AINIT65432.2025.11035133", "citation_count": 17, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3138}
{"id": "f1980afe315662812ba745f75368e70155ea29e68495b8ee6da9ce0f3fcfdeb0", "sources": ["arxiv", "semantic_scholar"], "title": "Ethical AI for Young Digital Citizens: A Call to Action on Privacy Governance", "abstract": "The rapid expansion of Artificial Intelligence (AI) in digital platforms used by youth has created significant challenges related to privacy, autonomy, and data protection. While AI-driven personalization offers enhanced user experiences, it often operates without clear ethical boundaries, leaving young users vulnerable to data exploitation and algorithmic biases. This paper presents a call to action for ethical AI governance, advocating for a structured framework that ensures youth-centred privacy protections, transparent data practices, and regulatory oversight. We outline key areas requiring urgent intervention, including algorithmic transparency, privacy education, parental data-sharing ethics, and accountability measures. Through this approach, we seek to empower youth with greater control over their digital identities and propose actionable strategies for policymakers, AI developers, and educators to build a fairer and more accountable AI ecosystem.", "authors": ["Austin Shouli", "Ankur Barthwal", "Molly Campbell", "Ajay Kumar Shrestha"], "categories": ["cs.CY", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-15", "url": "https://arxiv.org/abs/2503.11947", "pdf_url": "https://arxiv.org/pdf/2503.11947v4", "arxiv_id": "2503.11947", "doi": "10.1002/spy2.70202", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Security and Privacy", "quality_score": 0.2113}
{"id": "70f6aca18b5f96334a63e728993d153eade2593cb8b38e0f132aeb6935e432f5", "sources": ["arxiv", "semantic_scholar"], "title": "From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges", "abstract": "Federated Learning (FL) enables collaborative learning without directly sharing individual's raw data. FL can be implemented in either a centralized (server-based) or decentralized (peer-to-peer) manner. In this survey, we present a novel perspective: the fundamental difference between centralized FL (CFL) and decentralized FL (DFL) is not merely the network topology, but the underlying training protocol: separate aggregation vs. joint optimization. We argue that this distinction in protocol leads to significant differences in model utility, privacy preservation, and robustness to attacks. We systematically review and categorize existing works in both CFL and DFL according to the type of protocol they employ. This taxonomy provides deeper insights into prior research and clarifies how various approaches relate or differ. Through our analysis, we identify key gaps in the literature. In particular, we observe a surprising lack of exploration of DFL approaches based on distributed optimization methods, despite their potential advantages. We highlight this under-explored direction and call for more research on leveraging distributed optimization for federated learning. Overall, this work offers a comprehensive overview from centralized to decentralized FL, sheds new light on the core distinctions between approaches, and outlines open challenges and future directions for the field.", "authors": ["Qiongxiu Li", "Wenrui Yu", "Yufei Xia", "Jun Pang"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-10", "url": "https://arxiv.org/abs/2503.07505", "pdf_url": "https://arxiv.org/pdf/2503.07505v1", "arxiv_id": "2503.07505", "doi": "10.48550/arXiv.2503.07505", "citation_count": 8, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "859549dd753d63b73b5a5b798bfe0562a21a2e9b81f54f431907168d25782e88", "sources": ["arxiv", "semantic_scholar"], "title": "Right Reward Right Time for Federated Learning", "abstract": "Critical learning periods (CLPs) in federated learning (FL) refer to early stages during which low-quality contributions (e.g., sparse training data availability) can permanently impair the performance of the global model owned by the cloud server. However, existing incentive mechanisms typically assume temporal homogeneity, treating all training rounds as equally important, thereby failing to prioritize and attract high-quality contributions during CLPs. This inefficiency is compounded by information asymmetry due to privacy regulations, where the cloud lacks knowledge of client training capabilities, leading to adverse selection and moral hazard. Thus, in this article, we propose a time-aware contract-theoretic incentive framework, named Right Reward Right Time (R3T), to encourage client involvement, especially during CLPs, to maximize the utility of the cloud server. We formulate a cloud utility function that captures the trade-off between the achieved model performance and rewards allocated for clients' contributions, explicitly accounting for client heterogeneity in time and system capabilities, effort, and joining time. Then, we devise a CLP-aware incentive mechanism deriving an optimal contract design that satisfies individual rationality, incentive compatibility, and budget feasibility constraints, motivating rational clients to participate early and contribute efforts. By providing the right reward at the right time, our approach can attract the highest-quality contributions during CLPs. Simulation and proof-of-concept studies show that R3T mitigates information asymmetry, increases cloud utility, and yields superior economic efficiency compared to conventional incentive mechanisms. Our proof-of-concept results demonstrate up to a 47.6% reduction in the total number of clients and up to a 300% improvement in convergence time while achieving competitive test accuracy.", "authors": ["Thanh Linh Nguyen", "Dinh Thai Hoang", "Diep N. Nguyen", "Quoc-Viet Pham"], "categories": ["cs.LG", "cs.AI", "cs.DC", "cs.GT"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-10", "url": "https://arxiv.org/abs/2503.07869", "pdf_url": "https://arxiv.org/pdf/2503.07869v3", "arxiv_id": "2503.07869", "doi": "10.48550/arXiv.2503.07869", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "1a8cca681ccd7a99c063cf4cf73641753980f551868f35861002469628c648f7", "sources": ["arxiv", "semantic_scholar"], "title": "FedEM: A Privacy-Preserving Framework for Concurrent Utility Preservation in Federated Learning", "abstract": "Federated Learning (FL) enables collaborative training of models across distributed clients without sharing local data, addressing privacy concerns in decentralized systems. However, the gradient-sharing process exposes private data to potential leakage, compromising FL's privacy guarantees in real-world applications. To address this issue, we propose Federated Error Minimization (FedEM), a novel algorithm that incorporates controlled perturbations through adaptive noise injection. This mechanism effectively mitigates gradient leakage attacks while maintaining model performance. Experimental results on benchmark datasets demonstrate that FedEM significantly reduces privacy risks and preserves model accuracy, achieving a robust balance between privacy protection and utility preservation.", "authors": ["Mingcong Xu", "Xiaojin Zhang", "Wei Chen", "Hai Jin"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-08", "url": "https://arxiv.org/abs/2503.06021", "pdf_url": "https://arxiv.org/pdf/2503.06021v1", "arxiv_id": "2503.06021", "doi": "10.48550/arXiv.2503.06021", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0825}
{"id": "c36eaca532b52251da1fcf54d74fef0eb4347e899e83745386b120cd0f5f8f85", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy Preserving and Robust Aggregation for Cross-Silo Federated Learning in Non-IID Settings", "abstract": "Federated Averaging remains the most widely used aggregation strategy in federated learning due to its simplicity and scalability. However, its performance degrades significantly in non-IID data settings, where client distributions are highly imbalanced or skewed. Additionally, it relies on clients transmitting metadata, specifically the number of training samples, which introduces privacy risks and may conflict with regulatory frameworks like the European GDPR. In this paper, we propose a novel aggregation strategy that addresses these challenges by introducing class-aware gradient masking. Unlike traditional approaches, our method relies solely on gradient updates, eliminating the need for any additional client metadata, thereby enhancing privacy protection. Furthermore, our approach validates and dynamically weights client contributions based on class-specific importance, ensuring robustness against non-IID distributions, convergence prevention, and backdoor attacks. Extensive experiments on benchmark datasets demonstrate that our method not only outperforms FedAvg and other widely accepted aggregation strategies in non-IID settings but also preserves model integrity in adversarial scenarios. Our results establish the effectiveness of gradient masking as a practical and secure solution for federated learning.", "authors": ["Marco Arazzi", "Mert Cihangiroglu", "Antonino Nocera"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-06", "url": "https://arxiv.org/abs/2503.04451", "pdf_url": "https://arxiv.org/pdf/2503.04451v1", "arxiv_id": "2503.04451", "doi": "10.48550/arXiv.2503.04451", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0802}
{"id": "47716910c8aaea68e130f8eaedc8cc669ce67d3036f828e426d8e788443b5927", "sources": ["arxiv", "semantic_scholar"], "title": "Quantum-Inspired Privacy-Preserving Federated Learning Framework for Secure Dementia Classification", "abstract": "Dementia, a neurological disorder impacting millions globally, presents significant challenges in diagnosis and patient care. With the rise of privacy concerns and security threats in healthcare, federated learning (FL) has emerged as a promising approach to enable collaborative model training across decentralized datasets without exposing sensitive patient information. However, FL remains vulnerable to advanced security breaches such as gradient inversion and eavesdropping attacks. This paper introduces a novel framework that integrates federated learning with quantum-inspired encryption techniques for dementia classification, emphasizing privacy preservation and security. Leveraging quantum key distribution (QKD), the framework ensures secure transmission of model weights, protecting against unauthorized access and interception during training. The methodology utilizes a convolutional neural network (CNN) for dementia classification, with federated training conducted across distributed healthcare nodes, incorporating QKD-encrypted weight sharing to secure the aggregation process. Experimental evaluations conducted on MRI data from the OASIS dataset demonstrate that the proposed framework achieves identical accuracy levels to a baseline model while enhancing data security and reducing loss by almost 1% compared to the classical baseline model. The framework offers significant implications for democratizing access to AI-driven dementia diagnostics in low- and middle-income countries, addressing critical resource and privacy constraints. This work contributes a robust, scalable, and secure federated learning solution for healthcare applications, paving the way for broader adoption of quantum-inspired techniques in AI-driven medical research.", "authors": ["Gazi Tanbhir", "Md. Farhan Shahriyar"], "categories": ["cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-05", "url": "https://arxiv.org/abs/2503.03267", "pdf_url": "https://arxiv.org/pdf/2503.03267v1", "arxiv_id": "2503.03267", "doi": "10.1109/ECCE64574.2025.11013884", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "European Conference on Cognitive Ergonomics", "quality_score": 0.25}
{"id": "991a5307d37ac1279d098bdfaee6039512f0b8fcfb810361930bf9fe1e8ace83", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Aware Sequential Learning", "abstract": "In settings like vaccination registries, individuals act after observing others, and the resulting public records can expose private information. We study privacy-preserving sequential learning, where agents add endogenous noise to their reported actions to conceal private signals. Efficient social learning relies on information flow, seemingly in conflict with privacy. Surprisingly, with continuous signals and a fixed privacy budget $(ε)$, the optimal randomization strategy balances privacy and accuracy, accelerating learning to $Θ_ε(\\log n)$, faster than the nonprivate $Θ(\\sqrt{\\log n})$ rate. In the nonprivate baseline, the expected time to the first correct action and the number of incorrect actions diverge; under privacy with sufficiently small $ε$, both are finite. Privacy helps because, under the false state, agents more often receive signals contradicting the majority; randomization then asymmetrically amplifies the log-likelihood ratio, enhancing aggregation. In heterogeneous populations, an order-optimal $Θ(\\sqrt{n})$ rate is achievable when a subset of agents have low privacy budgets. With binary signals, however, privacy reduces informativeness and impairs learning relative to the nonprivate baseline, though the dependence on $ε$ is nonmonotone. Our results show how privacy reshapes information dynamics and inform the design of platforms and policies.", "authors": ["Yuxin Liu", "M. Amin Rahimian"], "categories": ["econ.TH", "cs.CR", "cs.SI", "math.PR", "stat.AP"], "fields_of_study": ["Economics", "Computer Science", "Mathematics"], "published_date": "2025-02-26", "url": "https://arxiv.org/abs/2502.19525", "pdf_url": "https://arxiv.org/pdf/2502.19525v5", "arxiv_id": "2502.19525", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/YuxinLiu1997/Privacy-Preserving-Sequential-Learning", "venue": null, "quality_score": 0.084}
{"id": "c98428d489005d471b3949436c1628ad987ce12fde170e56d5fb01f64d545cdc", "sources": ["arxiv", "semantic_scholar"], "title": "FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk", "abstract": "Federated Learning (FL) inherently mitigates mass data centralization risks; however, its privacy protections are not equally distributed - leaving vulnerable individuals disproportionately exposed to sophisticated privacy attacks. Crucially, statistical heterogeneity in human-centric FL environments often results in an inequitable distribution of privacy risks, particularly affecting those whose sensitive attributes or behaviors make them outliers. To address this critical gap, we introduce FinP, a novel framework designed to formalize and enforce fairness-in-privacy by mitigating disproportionate client vulnerability to Source Inference Attacks (SIA). FinP operationalizes a two-pronged defense strategy that tackles both the symptoms and root causes of privacy disparity, ensuring that no group of clients bears an excessive privacy burden. It combines a server-side adaptive aggregation mechanism, which dynamically weights client contributions based on their estimated privacy risk, with a client-side regularization technique to curb localized overfitting that drives unique data memorization. Extensive empirical evaluations on FEMNIST, Human Activity Recognition (HAR), and CIFAR-10 datasets demonstrate that FinP effectively aligns privacy fairness with primary task utility. Notably, FinP successfully mitigates SIA risks and reduces disparities in privacy exposure, establishing that strong fairness-in-privacy guarantees need not compromise model utility. Ultimately, FinP establishes equitable privacy protections by reducing vulnerability disparities by up to 57.14%, while preserving global model utility within a marginal +/- 1.75% of standard federated baselines.", "authors": ["Tianyu Zhao", "Mahmoud Srewa", "Salma Elmalaki"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-25", "url": "https://arxiv.org/abs/2502.17748", "pdf_url": "https://arxiv.org/pdf/2502.17748v4", "arxiv_id": "2502.17748", "doi": "10.48550/arXiv.2502.17748", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "53cdf55c7a331ac9827d0c781bd86f627dc859a6cfdd2e710897d54fba98669f", "sources": ["arxiv", "semantic_scholar"], "title": "Differentially Private Federated Learning With Time-Adaptive Privacy Spending", "abstract": "Federated learning (FL) with differential privacy (DP) provides a framework for collaborative machine learning, enabling clients to train a shared model while adhering to strict privacy constraints. The framework allows each client to have an individual privacy guarantee, e.g., by adding different amounts of noise to each client's model updates. One underlying assumption is that all clients spend their privacy budgets uniformly over time (learning rounds). However, it has been shown in the literature that learning in early rounds typically focuses on more coarse-grained features that can be learned at lower signal-to-noise ratios while later rounds learn fine-grained features that benefit from higher signal-to-noise ratios. Building on this intuition, we propose a time-adaptive DP-FL framework that expends the privacy budget non-uniformly across both time and clients. Our framework enables each client to save privacy budget in early rounds so as to be able to spend more in later rounds when additional accuracy is beneficial in learning more fine-grained features. We theoretically prove utility improvements in the case that clients with stricter privacy budgets spend budgets unevenly across rounds, compared to clients with more relaxed budgets, who have sufficient budgets to distribute their spend more evenly. Our practical experiments on standard benchmark datasets support our theoretical results and show that, in practice, our algorithms improve the privacy-utility trade-offs compared to baseline schemes.", "authors": ["Shahrzad Kiani", "Nupur Kulkarni", "Adam Dziedzic", "Stark Draper", "Franziska Boenisch"], "categories": ["cs.LG", "cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-25", "url": "https://arxiv.org/abs/2502.18706", "pdf_url": "https://arxiv.org/pdf/2502.18706v1", "arxiv_id": "2502.18706", "doi": "10.48550/arXiv.2502.18706", "citation_count": 15, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "International Conference on Learning Representations", "quality_score": 0.301}
{"id": "2893bafe1babd94bbea982f32e0b94dcff1efafdf6b1216285e1c28e7b308ac8", "sources": ["arxiv", "semantic_scholar"], "title": "Unveiling Client Privacy Leakage from Public Dataset Usage in Federated Distillation", "abstract": "Federated Distillation (FD) has emerged as a popular federated training framework, enabling clients to collaboratively train models without sharing private data. Public Dataset-Assisted Federated Distillation (PDA-FD), which leverages public datasets for knowledge sharing, has become widely adopted. Although PDA-FD enhances privacy compared to traditional Federated Learning, we demonstrate that the use of public datasets still poses significant privacy risks to clients' private training data. This paper presents the first comprehensive privacy analysis of PDA-FD in presence of an honest-but-curious server. We show that the server can exploit clients' inference results on public datasets to extract two critical types of private information: label distributions and membership information of the private training dataset. To quantify these vulnerabilities, we introduce two novel attacks specifically designed for the PDA-FD setting: a label distribution inference attack and innovative membership inference methods based on Likelihood Ratio Attack (LiRA). Through extensive evaluation of three representative PDA-FD frameworks (FedMD, DS-FL, and Cronus), our attacks achieve state-of-the-art performance, with label distribution attacks reaching minimal KL-divergence and membership inference attacks maintaining high True Positive Rates under low False Positive Rate constraints. Our findings reveal significant privacy risks in current PDA-FD frameworks and emphasize the need for more robust privacy protection mechanisms in collaborative learning systems.", "authors": ["Haonan Shi", "Tu Ouyang", "An Wang"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-11", "url": "https://arxiv.org/abs/2502.08001", "pdf_url": "https://arxiv.org/pdf/2502.08001v2", "arxiv_id": "2502.08001", "doi": "10.48550/arXiv.2502.08001", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Proceedings on Privacy Enhancing Technologies", "quality_score": 0.1747}
{"id": "cb95737935c2b3fbb67fe07c40db20ea5a6291fb31ae2e94f0199b48bff0d999", "sources": ["arxiv", "semantic_scholar"], "title": "Structure-preserving contrastive learning for spatial time series", "abstract": "The effectiveness of neural network models largely relies on learning meaningful latent patterns from data, where self-supervised learning of informative representations can enhance model performance and generalisability. However, self-supervised representation learning for spatially characterised time series, which are ubiquitous in transportation domain, poses unique challenges due to the necessity of maintaining fine-grained spatio-temporal similarities in the latent space. In this study, we introduce two structure-preserving regularisers for the contrastive learning of spatial time series: one regulariser preserves the topology of similarities between instances, and the other preserves the graph geometry of similarities across spatial and temporal dimensions. To balance the contrastive learning objective and the need for structure preservation, we propose a dynamic weighting mechanism that adaptively manages this trade-off and stabilises training. We validate the proposed method through extensive experiments, including multivariate time series classification to demonstrate its general applicability, as well as macroscopic and microscopic traffic prediction to highlight its particular usefulness in encoding traffic interactions. Across all tasks, our method preserves the similarity structures more effectively and improves state-of-the-art task performances. This method can be integrated with an arbitrary neural network model and is particularly beneficial for time series data with spatial or geographical features. Furthermore, our findings suggest that well-preserved similarity structures in the latent space indicate more informative and useful representations. This provides insights to design more effective neural networks for data-driven transportation research. Our code is made openly accessible with all resulting data at https://github.com/yiru-jiao/spclt", "authors": ["Yiru Jiao", "Sander van Cranenburgh", "Simeon Calvert", "Hans van Lint"], "categories": ["cs.LG", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-10", "url": "https://arxiv.org/abs/2502.06380", "pdf_url": "https://arxiv.org/pdf/2502.06380v5", "arxiv_id": "2502.06380", "doi": "10.1016/j.ait.2025.100031", "citation_count": 2, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/yiru-jiao/spclt", "venue": "Artificial Intelligence for Transportation, 2025, 100031", "quality_score": 0.1193}
{"id": "dcef246a637f522e9a4fbfd246680720bd7c0af01f2add9690881a649be12c15", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning With Individualized Privacy Through Client Sampling", "abstract": "With growing concerns about user data collection, individualized privacy has emerged as a promising solution to balance protection and utility by accounting for diverse user privacy preferences. Instead of enforcing a uniform level of anonymization for all users, this approach allows individuals to choose privacy settings that align with their comfort levels. Building on this idea, we propose an adapted method for enabling Individualized Differential Privacy (IDP) in Federated Learning (FL) by handling clients according to their personal privacy preferences. By extending the SAMPLE algorithm from centralized settings to FL, we calculate client-specific sampling rates based on their heterogeneous privacy budgets and integrate them into a modified IDP-FedAvg algorithm. We test this method under realistic privacy distributions and multiple datasets. The experimental results demonstrate that our approach achieves clear improvements over uniform DP baselines, reducing the trade-off between privacy and utility. Compared to the alternative SCALE method in related work, which assigns differing noise scales to clients, our method performs notably better. However, challenges remain for complex tasks with non-i.i.d. data, primarily stemming from the constraints of the decentralized setting.", "authors": ["Lucas Lange", "Ole Borchardt", "Erhard Rahm"], "categories": ["cs.LG", "cs.AI", "cs.CR", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-29", "url": "https://arxiv.org/abs/2501.17634", "pdf_url": "https://arxiv.org/pdf/2501.17634v2", "arxiv_id": "2501.17634", "doi": "10.1109/ICMLT65785.2025.11193238", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Machine Learning Technologies", "quality_score": 0.1193}
{"id": "df0476d2362e01c4c0fcb286f5a6dacfb926d89ed5da60f0f81ebc0892129bf3", "sources": ["arxiv", "semantic_scholar"], "title": "Optimal Strategies for Federated Learning Maintaining Client Privacy", "abstract": "Federated Learning (FL) emerged as a learning method to enable the server to train models over data distributed among various clients. These clients are protective about their data being leaked to the server, any other client, or an external adversary, and hence, locally train the model and share it with the server rather than sharing the data. The introduction of sophisticated inferencing attacks enabled the leakage of information about data through access to model parameters. To tackle this challenge, privacy-preserving federated learning aims to achieve differential privacy through learning algorithms like DP-SGD. However, such methods involve adding noise to the model, data, or gradients, reducing the model's performance. This work provides a theoretical analysis of the tradeoff between model performance and communication complexity of the FL system. We formally prove that training for one local epoch per global round of training gives optimal performance while preserving the same privacy budget. We also investigate the change of utility (tied to privacy) of FL models with a change in the number of clients and observe that when clients are training using DP-SGD and argue that for the same privacy budget, the utility improved with increased clients. We validate our findings through experiments on real-world datasets. The results from this paper aim to improve the performance of privacy-preserving federated learning systems.", "authors": ["Uday Bhaskar", "Varul Srivastava", "Avyukta Manjunatha Vummintala", "Naresh Manwani", "Sujit Gujar"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-24", "url": "https://arxiv.org/abs/2501.14453", "pdf_url": "https://arxiv.org/pdf/2501.14453v1", "arxiv_id": "2501.14453", "doi": "10.48550/arXiv.2501.14453", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0332}
{"id": "322ade57e77a7be3373fd049bc5b911539b3b2b43701f410cdfbf74d87ccc620", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models", "abstract": "Multimodal Large Language Models (LLMs) are pivotal in revolutionizing customer support and operations by integrating multiple modalities such as text, images, and audio. Federated Prompt Learning (FPL) is a recently proposed approach that combines pre-trained multimodal LLMs such as vision-language models with federated learning to create personalized, privacy-preserving AI systems. However, balancing the competing goals of personalization, generalization, and privacy remains a significant challenge. Over-personalization can lead to overfitting, reducing generalizability, while stringent privacy measures, such as differential privacy, can hinder both personalization and generalization. In this paper, we propose a Differentially Private Federated Prompt Learning (DP-FPL) approach to tackle this challenge by leveraging a low-rank factorization scheme to capture generalization while maintaining a residual term that preserves expressiveness for personalization. To ensure privacy, we introduce a novel method where we apply local differential privacy to the two low-rank components of the local prompt, and global differential privacy to the global prompt. Our approach mitigates the impact of privacy noise on the model performance while balancing the tradeoff between personalization and generalization. Extensive experiments demonstrate the effectiveness of our approach over other benchmarks.", "authors": ["Linh Tran", "Wei Sun", "Stacy Patterson", "Ana Milanova"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-23", "url": "https://arxiv.org/abs/2501.13904", "pdf_url": "https://arxiv.org/pdf/2501.13904v3", "arxiv_id": "2501.13904", "doi": "10.48550/arXiv.2501.13904", "citation_count": 11, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "International Conference on Learning Representations", "quality_score": 0.2698}
{"id": "6f067125ce00d4605e93f76e52bea90556059d3494b90c281f7665df07903a5c", "sources": ["arxiv", "semantic_scholar"], "title": "A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning", "abstract": "Federated learning (FL) has come forward as a critical approach for privacy-preserving machine learning in healthcare, allowing collaborative model training across decentralized medical datasets without exchanging clients' data. However, current security implementations for these systems face a fundamental trade-off: rigorous cryptographic protections like fully homomorphic encryption (FHE) impose prohibitive computational overhead, while lightweight alternatives risk vulnerable data leakage through model updates. To address this issue, we present FAS (Fast and Secure Federated Learning), a novel approach that strategically combines selective homomorphic encryption, differential privacy, and bitwise scrambling to achieve robust security without compromising practical usability. Our approach eliminates the need for model pretraining phases while dynamically protecting high-risk model parameters through layered encryption and obfuscation. We implemented FAS using the Flower framework and evaluated it on a cluster of eleven physical machines. Our approach was up to 90\\% faster than applying FHE on the model weights. In addition, we eliminated the computational overhead that is required by competitors such as FedML-HE and MaskCrypt. Our approach was up to 1.5$\\times$ faster than the competitors while achieving comparable security results. Experimental evaluations on medical imaging datasets confirm that FAS maintains similar security results to conventional FHE against gradient inversion attacks while preserving diagnostic model accuracy. These results position FAS as a practical solution for latency-sensitive healthcare applications where both privacy preservation and computational efficiency are requirements.", "authors": ["Abdulkadir Korkmaz", "Praveen Rao"], "categories": ["cs.CR", "cs.DC", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-22", "url": "https://arxiv.org/abs/2501.12911", "pdf_url": "https://arxiv.org/pdf/2501.12911v4", "arxiv_id": "2501.12911", "doi": "10.1109/CCNC65079.2026.11366371", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Consumer Communications and Networking Conference", "quality_score": 0.1747}
{"id": "2a6bf616b413fe391610120075db6a11a886eff66a37656ad0567c9d3e29137a", "sources": ["arxiv", "semantic_scholar"], "title": "TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning", "abstract": "Federated learning is a computing paradigm that enhances privacy by enabling multiple parties to collaboratively train a machine learning model without revealing personal data. However, current research indicates that traditional federated learning platforms are unable to ensure privacy due to privacy leaks caused by the interchange of gradients. To achieve privacy-preserving federated learning, integrating secure aggregation mechanisms is essential. Unfortunately, existing solutions are vulnerable to recently demonstrated inference attacks such as the disaggregation attack. This paper proposes TAPFed, an approach for achieving privacy-preserving federated learning in the context of multiple decentralized aggregators with malicious actors. TAPFed uses a proposed threshold functional encryption scheme and allows for a certain number of malicious aggregators while maintaining security and privacy. We provide formal security and privacy analyses of TAPFed and compare it to various baselines through experimental evaluation. Our results show that TAPFed offers equivalent performance in terms of model quality compared to state-of-the-art approaches while reducing transmission overhead by 29%-45% across different model training scenarios. Most importantly, TAPFed can defend against recently demonstrated inference attacks caused by curious aggregators, which the majority of existing approaches are susceptible to.", "authors": ["Runhua Xu", "Bo Li", "Chao Li", "James B. D. Joshi", "Shuai Ma", "Jianxin Li"], "categories": ["cs.CR", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-09", "url": "https://arxiv.org/abs/2501.05053", "pdf_url": "https://arxiv.org/pdf/2501.05053v1", "arxiv_id": "2501.05053", "doi": "10.1109/TDSC.2024.3350206", "citation_count": 36, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Dependable and Secure Computing", "quality_score": 0.3921}
{"id": "fef0b3a2b9108b59024bf79ee189ecfdb9c6cb73f4b3356b5c1e05a268bd4cbf", "sources": ["arxiv", "semantic_scholar"], "title": "An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack", "abstract": "In this paper, we empirically analyze adversarial attacks on selected federated learning models. The specific learning models considered are Multinominal Logistic Regression (MLR), Support Vector Classifier (SVC), Multilayer Perceptron (MLP), Convolution Neural Network (CNN), %Recurrent Neural Network (RNN), Random Forest, XGBoost, and Long Short-Term Memory (LSTM). For each model, we simulate label-flipping attacks, experimenting extensively with 10 federated clients and 100 federated clients. We vary the percentage of adversarial clients from 10% to 100% and, simultaneously, the percentage of labels flipped by each adversarial client is also varied from 10% to 100%. Among other results, we find that models differ in their inherent robustness to the two vectors in our label-flipping attack, i.e., the percentage of adversarial clients, and the percentage of labels flipped by each adversarial client. We discuss the potential practical implications of our results.", "authors": ["Kunal Bhatnagar", "Sagana Chattanathan", "Angela Dang", "Bhargav Eranki", "Ronnit Rana", "Charan Sridhar", "Siddharth Vedam", "Angie Yao", "Mark Stamp"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-24", "url": "https://arxiv.org/abs/2412.18507", "pdf_url": "https://arxiv.org/pdf/2412.18507v1", "arxiv_id": "2412.18507", "doi": "10.48550/arXiv.2412.18507", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "bf89495e8b649a3b03efca8ef5d724668ba3a1551734a330373754cefb67ed31", "sources": ["arxiv", "semantic_scholar"], "title": "Federal Learning Framework for Quality Evaluation of Blastomere Cleavage", "abstract": "This study addresses the issue of leveraging federated learning to improve data privacy and performance in IVF embryo selection. The EM (Expectation-Maximization) algorithm is incorporated into deep learning models to form a federated learning framework for quality evaluation of blastomere cleavage using two-dimensional images. The framework comprises a server site and several client sites characterized in that each is locally trained with an EM algorithm. Upon the completion of the local EM training, a separate 5-mode mixture distribution is generated for each client, the clients' distribution statics are then uploaded to the server site and aggregated therein to produce a global (sharing) 5-mode distribution. During the inference phase, each client uses image classifiers and an instance segmentor, assisted by the global 5-mode distribution acting as a calibrator to (1) identify the absolute cleavage timing of blastomere, i.e., tPNa, tPNf, t2, t3, t4, t5, t6, t7, and t8, (2) track the cleavage process of blastomeres to detect the irregular cleavage patterns, and (3) assess the symmetry degree of blastomeres. Experimental results show that the proposed method outperforms commercial Time-Lapse Incubators in reducing the average error of timing prediction by twofold. The proposed facilitate frameworks the adaptability and scalability of classifiers and segmentor to data variability associated with patients in different locations or countries.", "authors": ["Jung-Hua Wang", "Huai-Wen Chang", "Rong-Yu Wu", "Ting-Yuan Wang", "Ming-Jer Chen", "Yu-Chiao Yi"], "categories": ["eess.IV"], "fields_of_study": ["Engineering"], "published_date": "2024-12-21", "url": "https://arxiv.org/abs/2412.16567", "pdf_url": "https://arxiv.org/pdf/2412.16567v1", "arxiv_id": "2412.16567", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0753}
{"id": "a2cc18f06fa89e9f96a42d3bbd90bc8bb3281bbdec0a7ea754013075c317000d", "sources": ["arxiv", "semantic_scholar"], "title": "SplitFedZip: Learned Compression for Data Transfer Reduction in Split-Federated Learning", "abstract": "Federated Learning (FL) enables multiple clients to train a collaborative model without sharing their local data. Split Learning (SL) allows a model to be trained in a split manner across different locations. Split-Federated (SplitFed) learning is a more recent approach that combines the strengths of FL and SL. SplitFed minimizes the computational burden of FL by balancing computation across clients and servers, while still preserving data privacy. This makes it an ideal learning framework across various domains, especially in healthcare, where data privacy is of utmost importance. However, SplitFed networks encounter numerous communication challenges, such as latency, bandwidth constraints, synchronization overhead, and a large amount of data that needs to be transferred during the learning process. In this paper, we propose SplitFedZip -- a novel method that employs learned compression to reduce data transfer in SplitFed learning. Through experiments on medical image segmentation, we show that learned compression can provide a significant data communication reduction in SplitFed learning, while maintaining the accuracy of the final trained model. The implementation is available at: \\url{https://github.com/ChamaniS/SplitFedZip}.", "authors": ["Chamani Shiranthika", "Hadi Hadizadeh", "Parvaneh Saeedi", "Ivan V. Bajić"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-18", "url": "https://arxiv.org/abs/2412.17150", "pdf_url": "https://arxiv.org/pdf/2412.17150v1", "arxiv_id": "2412.17150", "doi": "10.48550/arXiv.2412.17150", "citation_count": 5, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/ChamaniS/SplitFedZip}", "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "13fbed6ad403d7d0f4f6c0afd4bc0752a85d7018d5bbc9e4eb234c825530b0dd", "sources": ["arxiv", "semantic_scholar"], "title": "Concurrent vertical and horizontal federated learning with fuzzy cognitive maps", "abstract": "Data privacy is a major concern in industries such as healthcare or finance. The requirement to safeguard privacy is essential to prevent data breaches and misuse, which can have severe consequences for individuals and organisations. Federated learning is a distributed machine learning approach where multiple participants collaboratively train a model without compromising the privacy of their data. However, a significant challenge arises from the differences in feature spaces among participants, known as non-IID data. This research introduces a novel federated learning framework employing fuzzy cognitive maps, designed to comprehensively address the challenges posed by diverse data distributions and non-identically distributed features in federated settings. The proposal is tested through several experiments using four distinct federation strategies: constant-based, accuracy-based, AUC-based, and precision-based weights. The results demonstrate the effectiveness of the approach in achieving the desired learning outcomes while maintaining privacy and confidentiality standards.", "authors": ["Jose L Salmeron", "Irina Arévalo"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-17", "url": "https://arxiv.org/abs/2412.12844", "pdf_url": "https://arxiv.org/pdf/2412.12844v1", "arxiv_id": "2412.12844", "doi": "10.48550/arXiv.2412.12844", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2113}
{"id": "0ab4f3ff5dc1847cf836af6eed51edc29850d4cc310341f582bbe25982bbf362", "sources": ["arxiv", "semantic_scholar"], "title": "Efficiently Achieving Secure Model Training and Secure Aggregation to Ensure Bidirectional Privacy-Preservation in Federated Learning", "abstract": "Bidirectional privacy-preservation federated learning is crucial as both local gradients and the global model may leak privacy. However, only a few works attempt to achieve it, and they often face challenges such as excessive communication and computational overheads, or significant degradation of model accuracy, which hinders their practical applications. In this paper, we design an efficient and high-accuracy bidirectional privacy-preserving scheme for federated learning to complete secure model training and secure aggregation. To efficiently achieve bidirectional privacy, we design an efficient and accuracy-lossless model perturbation method on the server side (called $\\mathbf{MP\\_Server}$) that can be combined with local differential privacy (LDP) to prevent clients from accessing the model, while ensuring that the local gradients obtained on the server side satisfy LDP. Furthermore, to ensure model accuracy, we customize a distributed differential privacy mechanism on the client side (called $\\mathbf{DDP\\_Client}$). When combined with $\\mathbf{MP\\_Server}$, it ensures LDP of the local gradients, while ensuring that the aggregated result matches the accuracy of central differential privacy (CDP). Extensive experiments demonstrate that our scheme significantly outperforms state-of-the-art bidirectional privacy-preservation baselines (SOTAs) in terms of computational cost, model accuracy, and defense ability against privacy attacks. Particularly, given target accuracy, the training time of SOTAs is approximately $200$ times, or even over $1000$ times, longer than that of our scheme. When the privacy budget is set relatively small, our scheme incurs less than $6\\%$ accuracy loss compared to the privacy-ignoring method, while SOTAs suffer up to $20\\%$ accuracy loss. Experimental results also show that the defense capability of our scheme outperforms than SOTAs.", "authors": ["Xue Yang", "Depan Peng", "Yan Feng", "Xiaohu Tang", "Weijun Fang", "Jun Shao"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-16", "url": "https://arxiv.org/abs/2412.11737", "pdf_url": "https://arxiv.org/pdf/2412.11737v1", "arxiv_id": "2412.11737", "doi": "10.48550/arXiv.2412.11737", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "88742b84bff7303e1a19cce2b41b38301c802a6036c97a6c0303be974e411f74", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks", "abstract": "Privacy-preserving federated learning (PPFL) aims to train a global model for multiple clients while maintaining their data privacy. However, current PPFL protocols exhibit one or more of the following insufficiencies: considerable degradation in accuracy, the requirement for sharing keys, and cooperation during the key generation or decryption processes. As a mitigation, we develop the first protocol that utilizes neural networks to implement PPFL, as well as incorporating an Aggregatable Hybrid Encryption scheme tailored to the needs of PPFL. We name these networks as Homomorphic Adversarial Networks (HANs) which demonstrate that neural networks are capable of performing tasks similar to multi-key homomorphic encryption (MK-HE) while solving the problems of key distribution and collaborative decryption. Our experiments show that HANs are robust against privacy attacks. Compared with non-private federated learning, experiments conducted on multiple datasets demonstrate that HANs exhibit a negligible accuracy loss (at most 1.35%). Compared to traditional MK-HE schemes, HANs increase encryption aggregation speed by 6,075 times while incurring a 29.2 times increase in communication overhead.", "authors": ["Wenhan Dong", "Chao Lin", "Xinlei He", "Shengmin Xu", "Xinyi Huang"], "categories": ["cs.CR", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-02", "url": "https://arxiv.org/abs/2412.01650", "pdf_url": "https://arxiv.org/pdf/2412.01650v3", "arxiv_id": "2412.01650", "doi": "10.48550/arXiv.2412.01650", "citation_count": 2, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Knowledge Science, Engineering and Management", "quality_score": 0.1505}
{"id": "670ad65fcacc71df89e9df2a7c71040bafde9ebd2322d14b7f1f30b8a0e5e5e8", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Privacy-Preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified ResNet Architecture", "abstract": "With increasing concerns over privacy in healthcare, especially for sensitive medical data, this research introduces a federated learning framework that combines local differential privacy and secure aggregation using Secure Multi-Party Computation for medical image classification. Further, we propose DPResNet, a modified ResNet architecture optimized for differential privacy. Leveraging the BloodMNIST benchmark dataset, we simulate a realistic data-sharing environment across different hospitals, addressing the distinct privacy challenges posed by federated healthcare data. Experimental results indicate that our privacy-preserving federated model achieves accuracy levels close to non-private models, surpassing traditional approaches while maintaining strict data confidentiality. By enhancing the privacy, efficiency, and reliability of healthcare data management, our approach offers substantial benefits to patients, healthcare providers, and the broader healthcare ecosystem.", "authors": ["Mohamad Haj Fares", "Ahmed Mohamed Saad Emam Saad"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-01", "url": "https://arxiv.org/abs/2412.00687", "pdf_url": "https://arxiv.org/pdf/2412.00687v1", "arxiv_id": "2412.00687", "doi": "10.48550/arXiv.2412.00687", "citation_count": 16, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3076}
{"id": "999f5b94a7f80dce590268679f4bcfb6dc784648b34b00390815bf51618a859d", "sources": ["arxiv", "semantic_scholar"], "title": "DP-CDA: An Algorithm for Enhanced Privacy Preservation in Dataset Synthesis Through Randomized Mixing", "abstract": "In recent years, the growth of data across various sectors, including healthcare, security, finance, and education, has created significant opportunities for analysis and informed decision-making. However, these datasets often contain sensitive and personal information, which raises serious privacy concerns. It has been shown in multiple works that a person's identity is intertwined with their data, even if the data is anonymized. Due to this lack of separation between a person's identity and their information, the patterns associated with an individual's information can uniquely identify them. Protecting individual privacy is crucial, yet many existing machine learning and data publishing algorithms struggle with high-dimensional data, facing challenges related to the trade-off between computational efficiency and privacy. To address these challenges, we introduce an effective data publishing algorithm \\emph{DP-CDA}. Our proposed algorithm generates synthetic data by randomly mixing the privacy-sensitive data in a class-specific manner and inducing carefully tuned randomness to ensure formal privacy guarantees. Our comprehensive privacy accounting shows that the proposed DP-CDA provides a stronger privacy guarantee compared to existing methods, allowing for better utility while maintaining a stricter level of privacy. To evaluate the effectiveness of DP-CDA, we examine the accuracy of predictive models trained on the synthetic data, which serves as a measure of dataset utility. Importantly, we identify an optimal order of mixing that balances privacy-utility trade-off. Our results indicate that synthetic datasets produced using the DP-CDA can achieve superior utility compared to those generated by conventional data publishing algorithms, even when subject to the same privacy requirements.", "authors": ["Utsab Saha", "Tanvir Muntakim Tonoy", "Hafiz Imtiaz"], "categories": ["stat.ML", "cs.LG"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-11-25", "url": "https://arxiv.org/abs/2411.16121", "pdf_url": "https://arxiv.org/pdf/2411.16121v3", "arxiv_id": "2411.16121", "doi": "10.1002/spy2.70207", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Security and Privacy", "quality_score": 0.0753}
{"id": "73ab88a5717167d4de840aa69f72836cec1072add1e4fe209c933339b39169d6", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sources", "abstract": "Federated Learning (FL) is a decentralized machine learning approach that has gained attention for its potential to enable collaborative model training across clients while protecting data privacy, making it an attractive solution for the chemical industry. This work aims to provide the chemical engineering community with an accessible introduction to the discipline. Supported by a hands-on tutorial and a comprehensive collection of examples, it explores the application of FL in tasks such as manufacturing optimization, multimodal data integration, and drug discovery while addressing the unique challenges of protecting proprietary information and managing distributed datasets. The tutorial was built using key frameworks such as $\\texttt{Flower}$ and $\\texttt{TensorFlow Federated}$ and was designed to provide chemical engineers with the right tools to adopt FL in their specific needs. We compare the performance of FL against centralized learning across three different datasets relevant to chemical engineering applications, demonstrating that FL will often maintain or improve classification performance, particularly for complex and heterogeneous data. We conclude with an outlook on the open challenges in federated learning to be tackled and current approaches designed to remediate and improve this framework.", "authors": ["Siddhant Dutta", "Iago Leal de Freitas", "Pedro Maciel Xavier", "Claudio Miceli de Farias", "David Esteban Bernal Neira"], "categories": ["cs.LG", "cs.DC", "cs.NE"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-23", "url": "https://arxiv.org/abs/2411.16737", "pdf_url": "https://arxiv.org/pdf/2411.16737v2", "arxiv_id": "2411.16737", "doi": "10.48550/arXiv.2411.16737", "citation_count": 8, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2386}
{"id": "39964fcb2388ff58a484d478345fde22187d45af7f51867aa4f66bb3be7c5412", "sources": ["arxiv", "semantic_scholar"], "title": "FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning", "abstract": "Federated Learning (FL), as a mainstream privacy-preserving machine learning paradigm, offers promising solutions for privacy-critical domains such as healthcare and finance. Although extensive efforts have been dedicated from both academia and industry to improve the vanilla FL, little work focuses on the data pricing mechanism. In contrast to the straightforward in/post-training pricing techniques, we study a more difficult problem of pre-training pricing without direct information from the learning process. We propose FLMarket that integrates a two-stage, auction-based pricing mechanism with a security protocol to address the utility-privacy conflict. Through comprehensive experiments, we show that the client selection according to FLMarket can achieve more than 10% higher accuracy in subsequent FL training compared to state-of-the-art methods. In addition, it outperforms the in-training baseline with more than 2% accuracy increase and 3x run-time speedup.", "authors": ["Zhenyu Wen", "Wanglei Feng", "Di Wu", "Haozhen Hu", "Chang Xu", "Bin Qian", "Zhen Hong", "Cong Wang", "Shouling Ji"], "categories": ["cs.LG", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-18", "url": "https://arxiv.org/abs/2411.11713", "pdf_url": "https://arxiv.org/pdf/2411.11713v1", "arxiv_id": "2411.11713", "doi": "10.1145/3690624.3709346", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Knowledge Discovery and Data Mining", "quality_score": 0.1747}
{"id": "bf1cf26e0aae43c02909fb9c8160d906199032a53beb5c4683248e5f5ee30a1f", "sources": ["arxiv", "semantic_scholar"], "title": "NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA", "abstract": "The Privacy Preserving Federated Learning Document VQA (PFL-DocVQA) competition challenged the community to develop provably private and communication-efficient solutions in a federated setting for a real-life use case: invoice processing. The competition introduced a dataset of real invoice documents, along with associated questions and answers requiring information extraction and reasoning over the document images. Thereby, it brings together researchers and expertise from the document analysis, privacy, and federated learning communities. Participants fine-tuned a pre-trained, state-of-the-art Document Visual Question Answering model provided by the organizers for this new domain, mimicking a typical federated invoice processing setup. The base model is a multi-modal generative language model, and sensitive information could be exposed through either the visual or textual input modality. Participants proposed elegant solutions to reduce communication costs while maintaining a minimum utility threshold in track 1 and to protect all information from each document provider using differential privacy in track 2. The competition served as a new testbed for developing and testing private federated learning methods, simultaneously raising awareness about privacy within the document image analysis and recognition community. Ultimately, the competition analysis provides best practices and recommendations for successfully running privacy-focused federated learning challenges in the future.", "authors": ["Marlon Tobaben", "Mohamed Ali Souibgui", "Rubèn Tito", "Khanh Nguyen", "Raouf Kerkouche", "Kangsoo Jung", "Joonas Jälkö", "Lei Kang", "Andrey Barsky", "Vincent Poulain d'Andecy", "Aurélie Joseph", "Aashiq Muhamed", "Kevin Kuo", "Virginia Smith", "Yusuke Yamasaki", "Takumi Fukami", "Kenta Niwa", "Iifan Tyou", "Hiro Ishii", "Rio Yokota", "Ragul N", "Rintu Kutum", "Josep Llados", "Ernest Valveny", "Antti Honkela", "Mario Fritz", "Dimosthenis Karatzas"], "categories": ["cs.LG", "cs.CR", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-06", "url": "https://arxiv.org/abs/2411.03730", "pdf_url": "https://arxiv.org/pdf/2411.03730v2", "arxiv_id": "2411.03730", "doi": "10.48550/arXiv.2411.03730", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Transactions on Machine Learning Research, ISSN 2835-8856, 2025", "quality_score": 0.1747}
{"id": "c3fd8c947b2178b44e1034c270a980fba30167da3362b3a9d0e06cf26fe33ac3", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Laundering", "abstract": "Combating money laundering has become increasingly complex with the rise of cybercrime and digitalization of financial transactions. Graph-based machine learning techniques have emerged as promising tools for Anti-Money Laundering (AML) detection, capturing intricate relationships within money laundering networks. However, the effectiveness of AML solutions is hindered by data silos within financial institutions, limiting collaboration and overall efficacy. This research presents a novel privacy-preserving approach for collaborative AML machine learning, facilitating secure data sharing across institutions and borders while preserving privacy and regulatory compliance. Leveraging Fully Homomorphic Encryption (FHE), computations are directly performed on encrypted data, ensuring the confidentiality of financial data. Notably, FHE over the Torus (TFHE) was integrated with graph-based machine learning using Zama Concrete ML. The research contributes two key privacy-preserving pipelines. First, the development of a privacy-preserving Graph Neural Network (GNN) pipeline was explored. Optimization techniques like quantization and pruning were used to render the GNN FHE-compatible. Second, a privacy-preserving graph-based XGBoost pipeline leveraging Graph Feature Preprocessor (GFP) was successfully developed. Experiments demonstrated strong predictive performance, with the XGBoost model consistently achieving over 99% accuracy, F1-score, precision, and recall on the balanced AML dataset in both unencrypted and FHE-encrypted inference settings. On the imbalanced dataset, the incorporation of graph-based features improved the F1-score by 8%. The research highlights the need to balance the trade-off between privacy and computational efficiency.", "authors": ["Fabrianne Effendi", "Anupam Chattopadhyay"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-05", "url": "https://arxiv.org/abs/2411.02926", "pdf_url": "https://arxiv.org/pdf/2411.02926v2", "arxiv_id": "2411.02926", "doi": "10.48550/arXiv.2411.02926", "citation_count": 11, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2698}
{"id": "d9fd82a176508a1d3af086c11ba19e59df19c7ac2bd2ba73dae82b7552cb4c2d", "sources": ["arxiv", "semantic_scholar"], "title": "FedBlock: A Blockchain Approach to Federated Learning against Backdoor Attacks", "abstract": "Federated Learning (FL) is a machine learning method for training with private data locally stored in distributed machines without gathering them into one place for central learning. Despite its promises, FL is prone to critical security risks. First, because FL depends on a central server to aggregate local training models, this is a single point of failure. The server might function maliciously. Second, due to its distributed nature, FL might encounter backdoor attacks by participating clients. They can poison the local model before submitting to the server. Either type of attack, on the server or the client side, would severely degrade learning accuracy. We propose FedBlock, a novel blockchain-based FL framework that addresses both of these security risks. FedBlock is uniquely desirable in that it involves only smart contract programming, thus deployable atop any blockchain network. Our framework is substantiated with a comprehensive evaluation study using real-world datasets. Its robustness against backdoor attacks is competitive with the literature of FL backdoor defense. The latter, however, does not address the server risk as we do.", "authors": ["Duong H. Nguyen", "Phi L. Nguyen", "Truong T. Nguyen", "Hieu H. Pham", "Duc A. Tran"], "categories": ["cs.CR", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-05", "url": "https://arxiv.org/abs/2411.02773", "pdf_url": "https://arxiv.org/pdf/2411.02773v1", "arxiv_id": "2411.02773", "doi": "10.1109/BigData62323.2024.10825703", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.1945}
{"id": "6432e62057e6835bc48ae3acb09245ba356fa371aaf830bd10fc22f44c7a5d82", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing", "abstract": "Federated Learning (FL) has become a key method for preserving data privacy in Internet of Things (IoT) environments, as it trains Machine Learning (ML) models locally while transmitting only model updates. Despite this design, FL remains susceptible to threats such as model inversion and membership inference attacks, which can reveal private training data. Differential Privacy (DP) techniques are often introduced to mitigate these risks, but simply injecting DP noise into black-box ML models can compromise accuracy, particularly in dynamic IoT contexts, where continuous, lifelong learning leads to excessive noise accumulation. To address this challenge, we propose Federated HyperDimensional computing with Privacy-preserving (FedHDPrivacy), an eXplainable Artificial Intelligence (XAI) framework that integrates neuro-symbolic computing and DP. Unlike conventional approaches, FedHDPrivacy actively monitors the cumulative noise across learning rounds and adds only the additional noise required to satisfy privacy constraints. In a real-world application for monitoring manufacturing machining processes, FedHDPrivacy maintains high performance while surpassing standard FL frameworks - Federated Averaging (FedAvg), Federated Proximal (FedProx), Federated Normalized Averaging (FedNova), and Federated Optimization (FedOpt) - by up to 37%. Looking ahead, FedHDPrivacy offers a promising avenue for further enhancements, such as incorporating multimodal data fusion.", "authors": ["Fardin Jalil Piran", "Zhiling Chen", "Mohsen Imani", "Farhad Imani"], "categories": ["cs.LG", "cs.AI", "cs.CR", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-11-02", "url": "https://arxiv.org/abs/2411.01140", "pdf_url": "https://arxiv.org/pdf/2411.01140v3", "arxiv_id": "2411.01140", "doi": "10.1016/j.compeleceng.2025.110261", "citation_count": 18, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Computers & electrical engineering", "quality_score": 0.3197}
{"id": "667f4e1b444f70ee8d4bc2ac05db63eaa2753c34f55e62fc85dfa67710eea74e", "sources": ["arxiv", "semantic_scholar"], "title": "Optimizing Federated Learning by Entropy-Based Client Selection", "abstract": "Although deep learning has revolutionized domains such as natural language processing and computer vision, its dependence on centralized datasets raises serious privacy concerns. Federated learning addresses this issue by enabling multiple clients to collaboratively train a global deep learning model without compromising their data privacy. However, the performance of such a model degrades under label skew, where the label distribution differs between clients. To overcome this issue, a novel method called FedEntOpt is proposed. In each round, it selects clients to maximize the entropy of the aggregated label distribution, ensuring that the global model is exposed to data from all available classes. Extensive experiments on multiple benchmark datasets show that the proposed method outperforms several state-of-the-art algorithms by up to 6% in classification accuracy under standard settings regardless of the model size, while achieving gains of over 30% in scenarios with low participation rates and client dropout. In addition, FedEntOpt offers the flexibility to be combined with existing algorithms, enhancing their classification accuracy by more than 40%. Importantly, its performance remains unaffected even when differential privacy is applied.", "authors": ["Andreas Lutz", "Gabriele Steidl", "Karsten Müller", "Wojciech Samek"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-02", "url": "https://arxiv.org/abs/2411.01240", "pdf_url": "https://arxiv.org/pdf/2411.01240v3", "arxiv_id": "2411.01240", "doi": "10.1109/FLTA67013.2025.11336673", "citation_count": 4, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1747}
{"id": "eeb7487d1cc55d44d08bde6834cc2b79a44de90ef0c579c6615a304b78a1204d", "sources": ["arxiv", "semantic_scholar"], "title": "PARDON: Privacy-Aware and Robust Federated Domain Generalization", "abstract": "Federated Learning (FL) shows promise in preserving privacy and enabling collaborative learning. However, most current solutions focus on private data collected from a single domain. A significant challenge arises when client data comes from diverse domains (i.e., domain shift), leading to poor performance on unseen domains. Existing Federated Domain Generalization approaches address this problem but assume each client holds data for an entire domain, limiting their practicality in real-world scenarios with domain-based heterogeneity and client sampling. In addition, certain methods enable information sharing among clients, raising privacy concerns as this information could be used to reconstruct sensitive private data. To overcome this, we introduce FISC, a novel FedDG paradigm designed to robustly handle more complicated domain distributions between clients while ensuring security. FISC enables learning across domains by extracting an interpolative style from local styles and employing contrastive learning. This strategy gives clients multi-domain representations and unbiased convergent targets. Empirical results on multiple datasets, including PACS, Office-Home, and IWildCam, show FISC outperforms state-of-the-art (SOTA) methods. Our method achieves accuracy on unseen domains, with improvements ranging from 3.64% to 57.22% on unseen domains. Our code is available at https://github.com/judydnguyen/PARDON-FedDG.", "authors": ["Dung Thuy Nguyen", "Taylor T. Johnson", "Kevin Leach"], "categories": ["cs.LG", "cs.CV", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-30", "url": "https://arxiv.org/abs/2410.22622", "pdf_url": "https://arxiv.org/pdf/2410.22622v2", "arxiv_id": "2410.22622", "doi": "10.1109/ICDCS63083.2025.00074", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/judydnguyen/PARDON-FedDG", "venue": "IEEE International Conference on Distributed Computing Systems", "quality_score": 0.0}
{"id": "e987d1ee39164a630c3287ebc422706e373227169954cec875a60831dd8a0891", "sources": ["arxiv", "semantic_scholar"], "title": "Comparative Evaluation of Clustered Federated Learning Methods", "abstract": "Over recent years, Federated Learning (FL) has proven to be one of the most promising methods of distributed learning which preserves data privacy. As the method evolved and was confronted to various real-world scenarios, new challenges have emerged. One such challenge is the presence of highly heterogeneous (often referred as non-IID) data distributions among participants of the FL protocol. A popular solution to this hurdle is Clustered Federated Learning (CFL), which aims to partition clients into groups where the distribution are homogeneous. In the literature, state-of-the-art CFL algorithms are often tested using a few cases of data heterogeneities, without systematically justifying the choices. Further, the taxonomy used for differentiating the different heterogeneity scenarios is not always straightforward. In this paper, we explore the performance of two state-of-theart CFL algorithms with respect to a proposed taxonomy of data heterogeneities in federated learning (FL). We work with three image classification datasets and analyze the resulting clusters against the heterogeneity classes using extrinsic clustering metrics. Our objective is to provide a clearer understanding of the relationship between CFL performances and data heterogeneity scenarios.", "authors": ["Michael Ben Ali", "Omar El-Rifai", "Imen Megdiche", "André Peninou", "Olivier Teste"], "categories": ["stat.ML", "cs.LG"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-10-18", "url": "https://arxiv.org/abs/2410.14212", "pdf_url": "https://arxiv.org/pdf/2410.14212v2", "arxiv_id": "2410.14212", "doi": "10.48550/arXiv.2410.14212", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "The 2nd IEEE International Conference on Federated Learning Technologies and Applications (FLTA24), Sep 2024, Valencia (Espagne), Spain", "quality_score": 0.0}
{"id": "9aff22b5f9195586527be1778f1a4d72e829583e3ce86fcbc369ab7f0707c8a6", "sources": ["arxiv", "semantic_scholar"], "title": "Disentangling data distribution for Federated Learning", "abstract": "Federated Learning (FL) facilitates collaborative training of a global model whose performance is boosted by private data owned by distributed clients, without compromising data privacy. Yet the wide applicability of FL is hindered by entanglement of data distributions across different clients. This paper demonstrates for the first time that by disentangling data distributions FL can in principle achieve efficiencies comparable to those of distributed systems, requiring only one round of communication. To this end, we propose a novel FedDistr algorithm, which employs stable diffusion models to decouple and recover data distributions. Empirical results on the CIFAR100 and DomainNet datasets show that FedDistr significantly enhances model utility and efficiency in both disentangled and near-disentangled scenarios while ensuring privacy, outperforming traditional federated learning methods.", "authors": ["Xinyuan Zhao", "Hanlin Gu", "Lixin Fan", "Yuxing Han", "Qiang Yang"], "categories": ["cs.DC", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-16", "url": "https://arxiv.org/abs/2410.12530", "pdf_url": "https://arxiv.org/pdf/2410.12530v2", "arxiv_id": "2410.12530", "doi": "10.48550/arXiv.2410.12530", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "b2af53abc04972636adaaea9c4d32608d035a0b4f394c28cbbb0a8f3d0c41086", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Privacy-Guaranteed Label Unlearning in Vertical Federated Learning: Few-Shot Forgetting without Disclosure", "abstract": "This paper addresses the critical challenge of unlearning in Vertical Federated Learning (VFL), a setting that has received far less attention than its horizontal counterpart. Specifically, we propose the first method tailored to \\textit{label unlearning} in VFL, where labels play a dual role as both essential inputs and sensitive information. To this end, we employ a representation-level manifold mixup mechanism to generate synthetic embeddings for both unlearned and retained samples. This is to provide richer signals for the subsequent gradient-based label forgetting and recovery steps. These augmented embeddings are then subjected to gradient-based label forgetting, effectively removing the associated label information from the model. To recover performance on the retained data, we introduce a recovery-phase optimization step that refines the remaining embeddings. This design achieves effective label unlearning while maintaining computational efficiency. We validate our method through extensive experiments on diverse datasets, including MNIST, CIFAR-10, CIFAR-100, ModelNet, Brain Tumor MRI, COVID-19 Radiography, and Yahoo Answers demonstrate strong efficacy and scalability. Overall, this work establishes a new direction for unlearning in VFL, showing that re-imagining mixup as an efficient mechanism can unlock practical and utility-preserving unlearning. The code is publicly available at https://github.com/bryanhx/Towards-Privacy-Guaranteed-Label-Unlearning-in-Vertical-Federated-Learning", "authors": ["Hanlin Gu", "Hong Xi Tae", "Lixin Fan", "Chee Seng Chan"], "categories": ["cs.LG", "cs.CR", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-14", "url": "https://arxiv.org/abs/2410.10922", "pdf_url": "https://arxiv.org/pdf/2410.10922v4", "arxiv_id": "2410.10922", "doi": null, "citation_count": 5, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/bryanhx/Towards-Privacy-Guaranteed-Label-Unlearning-in-Vertical-Federated-Learning", "venue": null, "quality_score": 0.1945}
{"id": "88e62efdfdecf6da42b59be140d004bcb67eaacbd6dfb329cd242538f1c7572f", "sources": ["arxiv", "semantic_scholar"], "title": "Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees", "abstract": "Personalized federated learning (PFL) offers a flexible framework for aggregating information across distributed clients with heterogeneous data. This work considers a personalized federated learning setting that simultaneously learns global and local models. While purely local training has no communication cost, collaborative learning among the clients can leverage shared knowledge to improve statistical accuracy, presenting an accuracy-communication trade-off in personalized federated learning. However, the theoretical analysis of how personalization quantitatively influences sample and algorithmic efficiency and their inherent trade-off is largely unexplored. This paper makes a contribution towards filling this gap, by providing a quantitative characterization of the personalization degree on the tradeoff. The results further offers theoretical insights for choosing the personalization degree. As a side contribution, we establish the minimax optimality in terms of statistical accuracy for a widely studied PFL formulation. The theoretical result is validated on both synthetic and real-world datasets and its generalizability is verified in a non-convex setting.", "authors": ["Xin Yu", "Zelin He", "Ying Sun", "Lingzhou Xue", "Runze Li"], "categories": ["stat.ML", "cs.DC", "cs.LG", "math.ST", "stat.CO"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-10-11", "url": "https://arxiv.org/abs/2410.08934", "pdf_url": "https://arxiv.org/pdf/2410.08934v4", "arxiv_id": "2410.08934", "doi": null, "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Machine Learning", "quality_score": 0.1193}
{"id": "d939da4e0ecff205e75b138fab216aa4c638802a285b454efd293de4bb104334", "sources": ["arxiv", "semantic_scholar"], "title": "Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System", "abstract": "The concept of a learning healthcare system (LHS) envisions a self-improving network where multimodal data from patient care are continuously analyzed to enhance future healthcare outcomes. However, realizing this vision faces significant challenges in data sharing and privacy protection. Privacy-Preserving Federated Learning (PPFL) is a transformative and promising approach that has the potential to address these challenges by enabling collaborative learning from decentralized data while safeguarding patient privacy. This paper proposes a vision for integrating PPFL into the healthcare ecosystem to achieve a truly LHS as defined by the Institute of Medicine (IOM) Roundtable.", "authors": ["Ravi Madduri", "Zilinghan Li", "Tarak Nandi", "Kibaek Kim", "Minseok Ryu", "Alex Rodriguez"], "categories": ["cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-29", "url": "https://arxiv.org/abs/2409.19756", "pdf_url": "https://arxiv.org/pdf/2409.19756v1", "arxiv_id": "2409.19756", "doi": "10.1109/TPS-ISA62245.2024.00039", "citation_count": 5, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "International Conference on Trust, Privacy and Security in Intelligent Systems and Applications", "quality_score": 0.1945}
{"id": "dde035c2e181795201f684f95878d44aff087558fac20d3740aa74c474dfa7ef", "sources": ["arxiv", "semantic_scholar"], "title": "In-depth Analysis of Privacy Threats in Federated Learning for Medical Data", "abstract": "Federated learning is emerging as a promising machine learning technique in the medical field for analyzing medical images, as it is considered an effective method to safeguard sensitive patient data and comply with privacy regulations. However, recent studies have revealed that the default settings of federated learning may inadvertently expose private training data to privacy attacks. Thus, the intensity of such privacy risks and potential mitigation strategies in the medical domain remain unclear. In this paper, we make three original contributions to privacy risk analysis and mitigation in federated learning for medical data. First, we propose a holistic framework, MedPFL, for analyzing privacy risks in processing medical data in the federated learning environment and developing effective mitigation strategies for protecting privacy. Second, through our empirical analysis, we demonstrate the severe privacy risks in federated learning to process medical images, where adversaries can accurately reconstruct private medical images by performing privacy attacks. Third, we illustrate that the prevalent defense mechanism of adding random noises may not always be effective in protecting medical images against privacy attacks in federated learning, which poses unique and pressing challenges related to protecting the privacy of medical data. Furthermore, the paper discusses several unique research questions related to the privacy protection of medical data in the federated learning environment. We conduct extensive experiments on several benchmark medical image datasets to analyze and mitigate the privacy risks associated with federated learning for medical data.", "authors": ["Badhan Chandra Das", "M. Hadi Amini", "Yanzhao Wu"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-27", "url": "https://arxiv.org/abs/2409.18907", "pdf_url": "https://arxiv.org/pdf/2409.18907v1", "arxiv_id": "2409.18907", "doi": "10.48550/arXiv.2409.18907", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "876b4f9b8fa3491ba8095c9fe09a178c405a327272561abad41de88ea2575c13", "sources": ["arxiv", "semantic_scholar"], "title": "Immersion and Invariance-based Coding for Privacy-Preserving Federated Learning", "abstract": "Federated learning (FL) has emerged as a method to preserve privacy in collaborative distributed learning. In FL, clients train AI models directly on their devices rather than sharing data with a centralized server, which can pose privacy risks. However, it has been shown that despite FL's partial protection of local data privacy, information about clients' data can still be inferred from shared model updates during training. In recent years, several privacy-preserving approaches have been developed to mitigate this privacy leakage in FL, though they often provide privacy at the cost of model performance or system efficiency. Balancing these trade-offs presents a significant challenge in implementing FL schemes. In this manuscript, we introduce a privacy-preserving FL framework that combines differential privacy and system immersion tools from control theory. The core idea is to treat the optimization algorithms used in standard FL schemes (e.g., gradient-based algorithms) as a dynamical system that we seek to immerse into a higher-dimensional system (referred to as the target optimization algorithm). The target algorithm's dynamics are designed such that, first, the model parameters of the original algorithm are immersed in its parameters; second, it operates on distorted parameters; and third, it converges to an encoded version of the true model parameters from the original algorithm. These encoded parameters can then be decoded at the server to retrieve the original model parameters. We demonstrate that the proposed privacy-preserving scheme can be tailored to offer any desired level of differential privacy for both local and global model parameters, while maintaining the same accuracy and convergence rate as standard FL algorithms.", "authors": ["Haleh Hayati", "Carlos Murguia", "Nathan van de Wouw"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-25", "url": "https://arxiv.org/abs/2409.17201", "pdf_url": "https://arxiv.org/pdf/2409.17201v2", "arxiv_id": "2409.17201", "doi": "10.48550/arXiv.2409.17201", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "73c2c40fca3f31cb841930020b024688e41ccb3af6b725db4268cd6146d87497", "sources": ["arxiv", "semantic_scholar"], "title": "Flotta: a Secure and Flexible Spark-inspired Federated Learning Framework", "abstract": "We present Flotta, a Federated Learning framework designed to train machine learning models on sensitive data distributed across a multi-party consortium conducting research in contexts requiring high levels of security, such as the biomedical field. Flotta is a Python package, inspired in several aspects by Apache Spark, which provides both flexibility and security and allows conducting research using solely machines internal to the consortium. In this paper, we describe the main components of the framework together with a practical use case to illustrate the framework's capabilities and highlight its security, flexibility and user-friendliness.", "authors": ["Claudio Bonesana", "Daniele Malpetti", "Sandra Mitrović", "Francesca Mangili", "Laura Azzimonti"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-20", "url": "https://arxiv.org/abs/2409.13473", "pdf_url": "https://arxiv.org/pdf/2409.13473v1", "arxiv_id": "2409.13473", "doi": "10.1109/FLTA63145.2024.10840050", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0753}
{"id": "dd457fae2452898cbc35f574932b55d9826b04dbe4560fe32b972976bd5b508f", "sources": ["arxiv", "semantic_scholar"], "title": "Global Outlier Detection in a Federated Learning Setting with Isolation Forest", "abstract": "We present a novel strategy for detecting global outliers in a federated learning setting, targeting in particular cross-silo scenarios. Our approach involves the use of two servers and the transmission of masked local data from clients to one of the servers. The masking of the data prevents the disclosure of sensitive information while still permitting the identification of outliers. Moreover, to further safeguard privacy, a permutation mechanism is implemented so that the server does not know which client owns any masked data point. The server performs outlier detection on the masked data, using either Isolation Forest or its extended version, and then communicates outlier information back to the clients, allowing them to identify and remove outliers in their local datasets before starting any subsequent federated model training. This approach provides comparable results to a centralized execution of Isolation Forest algorithms on plain data.", "authors": ["Daniele Malpetti", "Laura Azzimonti"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-20", "url": "https://arxiv.org/abs/2409.13466", "pdf_url": "https://arxiv.org/pdf/2409.13466v1", "arxiv_id": "2409.13466", "doi": "10.1109/FLTA63145.2024.10840168", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1193}
{"id": "2c0470d4dee1ec65207ebb530e34196f93000b61720f89cc96271fb37fdb4bfa", "sources": ["arxiv", "semantic_scholar"], "title": "Data Poisoning and Leakage Analysis in Federated Learning", "abstract": "Data poisoning and leakage risks impede the massive deployment of federated learning in the real world. This chapter reveals the truths and pitfalls of understanding two dominating threats: {\\em training data privacy intrusion} and {\\em training data poisoning}. We first investigate training data privacy threat and present our observations on when and how training data may be leaked during the course of federated training. One promising defense strategy is to perturb the raw gradient update by adding some controlled randomized noise prior to sharing during each round of federated learning. We discuss the importance of determining the proper amount of randomized noise and the proper location to add such noise for effective mitigation of gradient leakage threats against training data privacy. Then we will review and compare different training data poisoning threats and analyze why and when such data poisoning induced model Trojan attacks may lead to detrimental damage on the performance of the global model. We will categorize and compare representative poisoning attacks and the effectiveness of their mitigation techniques, delivering an in-depth understanding of the negative impact of data poisoning. Finally, we demonstrate the potential of dynamic model perturbation in simultaneously ensuring privacy protection, poisoning resilience, and model performance. The chapter concludes with a discussion on additional risk factors in federated learning, including the negative impact of skewness, data and algorithmic biases, as well as misinformation in training data. Powered by empirical evidence, our analytical study offers some transformative insights into effective privacy protection and security assurance strategies in attack-resilient federated learning.", "authors": ["Wenqi Wei", "Tiansheng Huang", "Zachary Yahn", "Anoop Singhal", "Margaret Loper", "Ling Liu"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-19", "url": "https://arxiv.org/abs/2409.13004", "pdf_url": "https://arxiv.org/pdf/2409.13004v1", "arxiv_id": "2409.13004", "doi": "10.1007/978-3-031-58923-2_3", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "b51ec9a189ac41166b1bf376c920917eb06ba05bd9aea97d1f0e9f8dd4f022f8", "sources": ["arxiv", "semantic_scholar"], "title": "Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data", "abstract": "This extended abstract explores the integration of federated learning with deep transfer hashing for distributed prediction tasks, emphasizing resource-efficient client training from evolving data streams. Federated learning allows multiple clients to collaboratively train a shared model while maintaining data privacy - by incorporating deep transfer hashing, high-dimensional data can be converted into compact hash codes, reducing data transmission size and network loads. The proposed framework utilizes transfer learning, pre-training deep neural networks on a central server, and fine-tuning on clients to enhance model accuracy and adaptability. A selective hash code sharing mechanism using a privacy-preserving global memory bank further supports client fine-tuning. This approach addresses challenges in previous research by improving computational efficiency and scalability. Practical applications include Car2X event predictions, where a shared model is collectively trained to recognize traffic patterns, aiding in tasks such as traffic density assessment and accident detection. The research aims to develop a robust framework that combines federated learning, deep transfer hashing and transfer learning for efficient and secure downstream task execution.", "authors": ["Manuel Röder", "Frank-Michael Schleif"], "categories": ["cs.LG", "cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-19", "url": "https://arxiv.org/abs/2409.12575", "pdf_url": "https://arxiv.org/pdf/2409.12575v1", "arxiv_id": "2409.12575", "doi": "10.48550/arXiv.2409.12575", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0}
{"id": "a036553d41a49fa57c42f651062d563d06229038bad92f966f26f8af60445efc", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator", "abstract": "Federated Learning (FL) is gaining popularity as a distributed learning framework that only shares model parameters or gradient updates and keeps private data locally. However, FL is at risk of privacy leakage caused by privacy inference attacks. And most existing privacy-preserving mechanisms in FL conflict with achieving high performance and efficiency. Therefore, we propose FedMD-CG, a novel FL method with highly competitive performance and high-level privacy preservation, which decouples each client's local model into a feature extractor and a classifier, and utilizes a conditional generator instead of the feature extractor to perform server-side model aggregation. To ensure the consistency of local generators and classifiers, FedMD-CG leverages knowledge distillation to train local models and generators at both the latent feature level and the logit level. Also, we construct additional classification losses and design new diversity losses to enhance client-side training. FedMD-CG is robust to data heterogeneity and does not require training extra discriminators (like cGAN). We conduct extensive experiments on various image classification tasks to validate the superiority of FedMD-CG.", "authors": ["Kangyang Luo", "Shuai Wang", "Xiang Li", "Yunshi Lan", "Ming Gao", "Jinlong Shu"], "categories": ["cs.LG", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-11", "url": "https://arxiv.org/abs/2409.06955", "pdf_url": "https://arxiv.org/pdf/2409.06955v2", "arxiv_id": "2409.06955", "doi": "10.48550/arXiv.2409.06955", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "da3176728c186e3d4604f510c332aac0f53dfb431b383d93ea9a0b9e76192fd0", "sources": ["arxiv", "semantic_scholar"], "title": "Buffer-based Gradient Projection for Continual Federated Learning", "abstract": "Continual Federated Learning (CFL) is essential for enabling real-world applications where multiple decentralized clients adaptively learn from continuous data streams. A significant challenge in CFL is mitigating catastrophic forgetting, where models lose previously acquired knowledge when learning new information. Existing approaches often face difficulties due to the constraints of device storage capacities and the heterogeneous nature of data distributions among clients. While some CFL algorithms have addressed these challenges, they frequently rely on unrealistic assumptions about the availability of task boundaries (i.e., knowing when new tasks begin). To address these limitations, we introduce Fed-A-GEM, a federated adaptation of the A-GEM method (Chaudhry et al., 2019), which employs a buffer-based gradient projection approach. Fed-A-GEM alleviates catastrophic forgetting by leveraging local buffer samples and aggregated buffer gradients, thus preserving knowledge across multiple clients. Our method is combined with existing CFL techniques, enhancing their performance in the CFL context. Our experiments on standard benchmarks show consistent performance improvements across diverse scenarios. For example, in a task-incremental learning scenario using the CIFAR-100 dataset, our method can increase the accuracy by up to 27%. Our code is available at https://github.com/shenghongdai/Fed-A-GEM.", "authors": ["Shenghong Dai", "Jy-yong Sohn", "Yicong Chen", "S M Iftekharul Alam", "Ravikumar Balakrishnan", "Suman Banerjee", "Nageen Himayat", "Kangwook Lee"], "categories": ["cs.LG", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-03", "url": "https://arxiv.org/abs/2409.01585", "pdf_url": "https://arxiv.org/pdf/2409.01585v1", "arxiv_id": "2409.01585", "doi": "10.48550/arXiv.2409.01585", "citation_count": 4, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/shenghongdai/Fed-A-GEM", "venue": null, "quality_score": 0.1747}
{"id": "eac7d37eedfc108123b61091580aa598474de47d4ccea90f17a9869ee83e9dcd", "sources": ["arxiv", "semantic_scholar"], "title": "Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications", "abstract": "Deploying federated learning (FL) in real-world scenarios, particularly in healthcare, poses challenges in communication and security. In particular, with respect to the federated aggregation procedure, researchers have been focusing on the study of secure aggregation (SA) schemes to provide privacy guarantees over the model's parameters transmitted by the clients. Nevertheless, the practical availability of SA in currently available FL frameworks is currently limited, due to computational and communication bottlenecks. To fill this gap, this study explores the implementation of SA within the open-source Fed-BioMed framework. We implement and compare two SA protocols, Joye-Libert (JL) and Low Overhead Masking (LOM), by providing extensive benchmarks in a panel of healthcare data analysis problems. Our theoretical and experimental evaluations on four datasets demonstrate that SA protocols effectively protect privacy while maintaining task accuracy. Computational overhead during training is less than 1% on a CPU and less than 50% on a GPU for large models, with protection phases taking less than 10 seconds. Incorporating SA into Fed-BioMed impacts task accuracy by no more than 2% compared to non-SA scenarios. Overall this study demonstrates the feasibility of SA in real-world healthcare applications and contributes in reducing the gap towards the adoption of privacy-preserving technologies in sensitive applications.", "authors": ["Riccardo Taiello", "Sergen Cansiz", "Marc Vesin", "Francesco Cremonesi", "Lucia Innocenti", "Melek Önen", "Marco Lorenzi"], "categories": ["cs.CR", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-02", "url": "https://arxiv.org/abs/2409.00974", "pdf_url": "https://arxiv.org/pdf/2409.00974v1", "arxiv_id": "2409.00974", "doi": "10.48550/arXiv.2409.00974", "citation_count": 8, "influential_citation_count": 2, "has_code": true, "code_url": null, "venue": null, "quality_score": 0.2386}
{"id": "23057af213efe2eab54b612098b9f0b77faa1d104b97581d9c4a2ac478c49e9c", "sources": ["arxiv", "semantic_scholar"], "title": "Seamless Integration: Sampling Strategies in Federated Learning Systems", "abstract": "Federated Learning (FL) represents a paradigm shift in the field of machine learning, offering an approach for a decentralized training of models across a multitude of devices while maintaining the privacy of local data. However, the dynamic nature of FL systems, characterized by the ongoing incorporation of new clients with potentially diverse data distributions and computational capabilities, poses a significant challenge to the stability and efficiency of these distributed learning networks. The seamless integration of new clients is imperative to sustain and enhance the performance and robustness of FL systems. This paper looks into the complexities of integrating new clients into existing FL systems and explores how data heterogeneity and varying data distribution (not independent and identically distributed) among them can affect model training, system efficiency, scalability and stability. Despite these challenges, the integration of new clients into FL systems presents opportunities to enhance data diversity, improve learning performance, and leverage distributed computational power. In contrast to other fields of application such as the distributed optimization of word predictions on Gboard (where federated learning once originated), there are usually only a few clients in the production environment, which is why information from each new client becomes all the more valuable. This paper outlines strategies for effective client selection strategies and solutions for ensuring system scalability and stability. Using the example of images from optical quality inspection, it offers insights into practical approaches. In conclusion, this paper proposes that addressing the challenges presented by new client integration is crucial to the advancement and efficiency of distributed learning networks, thus paving the way for the adoption of Federated Learning in production environments.", "authors": ["Tatjana Legler", "Vinit Hegiste", "Martin Ruskowski"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-18", "url": "https://arxiv.org/abs/2408.09545", "pdf_url": "https://arxiv.org/pdf/2408.09545v2", "arxiv_id": "2408.09545", "doi": "10.1109/FLTA63145.2024.10840172", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1505}
{"id": "acf252f00df7e55ad82f8f694044d907cc3256343f2974aff56f68d65ea54981", "sources": ["arxiv", "semantic_scholar"], "title": "A Multivocal Literature Review on Privacy and Fairness in Federated Learning", "abstract": "Federated Learning presents a way to revolutionize AI applications by eliminating the necessity for data sharing. Yet, research has shown that information can still be extracted during training, making additional privacy-preserving measures such as differential privacy imperative. To implement real-world federated learning applications, fairness, ranging from a fair distribution of performance to non-discriminative behaviour, must be considered. Particularly in high-risk applications (e.g. healthcare), avoiding the repetition of past discriminatory errors is paramount. As recent research has demonstrated an inherent tension between privacy and fairness, we conduct a multivocal literature review to examine the current methods to integrate privacy and fairness in federated learning. Our analyses illustrate that the relationship between privacy and fairness has been neglected, posing a critical risk for real-world applications. We highlight the need to explore the relationship between privacy, fairness, and performance, advocating for the creation of integrated federated learning frameworks.", "authors": ["Beatrice Balbierer", "Lukas Heinlein", "Domenique Zipperling", "Niklas Kühl"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-16", "url": "https://arxiv.org/abs/2408.08666", "pdf_url": "https://arxiv.org/pdf/2408.08666v2", "arxiv_id": "2408.08666", "doi": "10.48550/arXiv.2408.08666", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Wirtschaftsinformatik", "quality_score": 0.1193}
{"id": "69482f4190ccaf7293df61c501a350a32b5c4dad132c0e5bf237892316dce98b", "sources": ["arxiv", "semantic_scholar"], "title": "An Adaptive Differential Privacy Method Based on Federated Learning", "abstract": "Differential privacy is one of the methods to solve the problem of privacy protection in federated learning. Setting the same privacy budget for each round will result in reduced accuracy in training. The existing methods of the adjustment of privacy budget consider fewer influencing factors and tend to ignore the boundaries, resulting in unreasonable privacy budgets. Therefore, we proposed an adaptive differential privacy method based on federated learning. The method sets the adjustment coefficient and scoring function according to accuracy, loss, training rounds, and the number of datasets and clients. And the privacy budget is adjusted based on them. Then the local model update is processed according to the scaling factor and the noise. Fi-nally, the server aggregates the noised local model update and distributes the noised global model. The range of parameters and the privacy of the method are analyzed. Through the experimental evaluation, it can reduce the privacy budget by about 16%, while the accuracy remains roughly the same.", "authors": ["Zhiqiang Wang", "Xinyue Yu", "Qianli Huang", "Yongguang Gong"], "categories": ["cs.CR", "cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-13", "url": "https://arxiv.org/abs/2408.08909", "pdf_url": "https://arxiv.org/pdf/2408.08909v1", "arxiv_id": "2408.08909", "doi": "10.48550/arXiv.2408.08909", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "fc292dab9ef66651088d4c8162392b37371d41658a4487148eb798ea1b091133", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy in Federated Learning", "abstract": "Federated Learning (FL) represents a significant advancement in distributed machine learning, enabling multiple participants to collaboratively train models without sharing raw data. This decentralized approach enhances privacy by keeping data on local devices. However, FL introduces new privacy challenges, as model updates shared during training can inadvertently leak sensitive information. This chapter delves into the core privacy concerns within FL, including the risks of data reconstruction, model inversion attacks, and membership inference. It explores various privacy-preserving techniques, such as Differential Privacy (DP) and Secure Multi-Party Computation (SMPC), which are designed to mitigate these risks. The chapter also examines the trade-offs between model accuracy and privacy, emphasizing the importance of balancing these factors in practical implementations. Furthermore, it discusses the role of regulatory frameworks, such as GDPR, in shaping the privacy standards for FL. By providing a comprehensive overview of the current state of privacy in FL, this chapter aims to equip researchers and practitioners with the knowledge necessary to navigate the complexities of secure federated learning environments. The discussion highlights both the potential and limitations of existing privacy-enhancing techniques, offering insights into future research directions and the development of more robust solutions.", "authors": ["Jaydip Sen", "Hetvi Waghela", "Sneha Rakshit"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-12", "url": "https://arxiv.org/abs/2408.08904", "pdf_url": "https://arxiv.org/pdf/2408.08904v1", "arxiv_id": "2408.08904", "doi": "10.5772/intechopen.1003421", "citation_count": 15, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.301}
{"id": "02555f018c7bfa30343479107f9990084a2d609d28f7da30fde07842c9f17751", "sources": ["arxiv", "semantic_scholar"], "title": "Lancelot: Towards Efficient and Privacy-Preserving Byzantine-Robust Federated Learning within Fully Homomorphic Encryption", "abstract": "In sectors such as finance and healthcare, where data governance is subject to rigorous regulatory requirements, the exchange and utilization of data are particularly challenging. Federated Learning (FL) has risen as a pioneering distributed machine learning paradigm that enables collaborative model training across multiple institutions while maintaining data decentralization. Despite its advantages, FL is vulnerable to adversarial threats, particularly poisoning attacks during model aggregation, a process typically managed by a central server. However, in these systems, neural network models still possess the capacity to inadvertently memorize and potentially expose individual training instances. This presents a significant privacy risk, as attackers could reconstruct private data by leveraging the information contained in the model itself. Existing solutions fall short of providing a viable, privacy-preserving BRFL system that is both completely secure against information leakage and computationally efficient. To address these concerns, we propose Lancelot, an innovative and computationally efficient BRFL framework that employs fully homomorphic encryption (FHE) to safeguard against malicious client activities while preserving data privacy. Our extensive testing, which includes medical imaging diagnostics and widely-used public image datasets, demonstrates that Lancelot significantly outperforms existing methods, offering more than a twenty-fold increase in processing speed, all while maintaining data privacy.", "authors": ["Siyang Jiang", "Hao Yang", "Qipeng Xie", "Chuan Ma", "Sen Wang", "Guoliang Xing"], "categories": ["cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-12", "url": "https://arxiv.org/abs/2408.06197", "pdf_url": "https://arxiv.org/pdf/2408.06197v1", "arxiv_id": "2408.06197", "doi": "10.48550/arXiv.2408.06197", "citation_count": 12, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2785}
{"id": "e77bff5686541a56899aa65b98984e4f4e941cdd25fb5899896e2f65536a9ab1", "sources": ["arxiv", "semantic_scholar"], "title": "Preserving Privacy in Large Language Models: A Survey on Current Threats and Solutions", "abstract": "Large Language Models (LLMs) represent a significant advancement in artificial intelligence, finding applications across various domains. However, their reliance on massive internet-sourced datasets for training brings notable privacy issues, which are exacerbated in critical domains (e.g., healthcare). Moreover, certain application-specific scenarios may require fine-tuning these models on private data. This survey critically examines the privacy threats associated with LLMs, emphasizing the potential for these models to memorize and inadvertently reveal sensitive information. We explore current threats by reviewing privacy attacks on LLMs and propose comprehensive solutions for integrating privacy mechanisms throughout the entire learning pipeline. These solutions range from anonymizing training datasets to implementing differential privacy during training or inference and machine unlearning after training. Our comprehensive review of existing literature highlights ongoing challenges, available tools, and future directions for preserving privacy in LLMs. This work aims to guide the development of more secure and trustworthy AI systems by providing a thorough understanding of privacy preservation methods and their effectiveness in mitigating risks.", "authors": ["Michele Miranda", "Elena Sofia Ruzzetti", "Andrea Santilli", "Fabio Massimo Zanzotto", "Sébastien Bratières", "Emanuele Rodolà"], "categories": ["cs.CR", "cs.AI", "cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-10", "url": "https://arxiv.org/abs/2408.05212", "pdf_url": "https://arxiv.org/pdf/2408.05212v2", "arxiv_id": "2408.05212", "doi": "10.48550/arXiv.2408.05212", "citation_count": 24, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3495}
{"id": "023f623097a4b612af5343f11b854aa2a4711726a4cad9ed747715aa6dbd55d4", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Hypergraph Learning with Local Differential Privacy: Toward Privacy-Aware Hypergraph Structure Completion", "abstract": "The rapid growth of graph-structured data necessitates partitioning and distributed storage across decentralized systems, driving the emergence of federated graph learning to collaboratively train Graph Neural Networks (GNNs) without compromising privacy. However, current methods exhibit limited performance when handling hypergraphs, which inherently represent complex high-order relationships beyond pairwise connections. Partitioning hypergraph structures across federated subsystems amplifies structural complexity, hindering high-order information mining and compromising local information integrity. To bridge the gap between hypergraph learning and federated systems, we develop FedHGL, a first-of-its-kind framework for federated hypergraph learning on disjoint and privacy-constrained hypergraph partitions. Beyond collaboratively training a comprehensive hypergraph neural network across multiple clients, FedHGL introduces a pre-propagation hyperedge completion mechanism to preserve high-order structural integrity within each client. This procedure leverages the federated central server to perform cross-client hypergraph convolution without exposing internal topological information, effectively mitigating the high-order information loss induced by subgraph partitioning. Furthermore, by incorporating two kinds of local differential privacy (LDP) mechanisms, we provide formal privacy guarantees for this process, ensuring that sensitive node features remain protected against inference attacks from potentially malicious servers or clients. Experimental results on seven real-world datasets confirm the effectiveness of our approach and demonstrate its performance advantages over traditional federated graph learning methods.", "authors": ["Linfeng Luo", "Zhiqi Guo", "Fengxiao Tang", "Zihao Qiu", "Ming Zhao"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-09", "url": "https://arxiv.org/abs/2408.05160", "pdf_url": "https://arxiv.org/pdf/2408.05160v3", "arxiv_id": "2408.05160", "doi": "10.1109/ICDM65498.2025.00149", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Industrial Conference on Data Mining", "quality_score": 0.0}
{"id": "d71a6d104c635b41c6746018bf991dc8f733ec48a25ef65452f5a7352cbe881f", "sources": ["arxiv", "semantic_scholar"], "title": "Fed-RD: Privacy-Preserving Federated Learning for Financial Crime Detection", "abstract": "We introduce Federated Learning for Relational Data (Fed-RD), a novel privacy-preserving federated learning algorithm specifically developed for financial transaction datasets partitioned vertically and horizontally across parties. Fed-RD strategically employs differential privacy and secure multiparty computation to guarantee the privacy of training data. We provide theoretical analysis of the end-to-end privacy of the training algorithm and present experimental results on realistic synthetic datasets. Our results demonstrate that Fed-RD achieves high model accuracy with minimal degradation as privacy increases, while consistently surpassing benchmark results.", "authors": ["Md. Saikat Islam Khan", "Aparna Gupta", "Oshani Seneviratne", "Stacy Patterson"], "categories": ["cs.CE"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-03", "url": "https://arxiv.org/abs/2408.01609", "pdf_url": "https://arxiv.org/pdf/2408.01609v1", "arxiv_id": "2408.01609", "doi": "10.1109/CIFEr62890.2024.10772978", "citation_count": 11, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "IEEE Conference on Computational Intelligence for Financial Engineering & Economics", "quality_score": 0.2698}
{"id": "a5a86791e29191764cf8194488ae1e540f9a6c412946b7af50e58afd25afdebd", "sources": ["arxiv", "semantic_scholar"], "title": "Theoretical Analysis of Privacy Leakage in Trustworthy Federated Learning: A Perspective from Linear Algebra and Optimization Theory", "abstract": "Federated learning has emerged as a promising paradigm for collaborative model training while preserving data privacy. However, recent studies have shown that it is vulnerable to various privacy attacks, such as data reconstruction attacks. In this paper, we provide a theoretical analysis of privacy leakage in federated learning from two perspectives: linear algebra and optimization theory. From the linear algebra perspective, we prove that when the Jacobian matrix of the batch data is not full rank, there exist different batches of data that produce the same model update, thereby ensuring a level of privacy. We derive a sufficient condition on the batch size to prevent data reconstruction attacks. From the optimization theory perspective, we establish an upper bound on the privacy leakage in terms of the batch size, the distortion extent, and several other factors. Our analysis provides insights into the relationship between privacy leakage and various aspects of federated learning, offering a theoretical foundation for designing privacy-preserving federated learning algorithms.", "authors": ["Xiaojin Zhang", "Wei Chen"], "categories": ["cs.CR", "cs.AI", "cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-07-23", "url": "https://arxiv.org/abs/2407.16735", "pdf_url": "https://arxiv.org/pdf/2407.16735v1", "arxiv_id": "2407.16735", "doi": "10.48550/arXiv.2407.16735", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "745a3245e16f24d5b3fa08c6410438e2a2d2b0409e0f0156b6c3b4e3f4e0304a", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-preserving gradient-based fair federated learning", "abstract": "Federated learning (FL) schemes allow multiple participants to collaboratively train neural networks without the need to directly share the underlying data.However, in early schemes, all participants eventually obtain the same model. Moreover, the aggregation is typically carried out by a third party, who obtains combined gradients or weights, which may reveal the model. These downsides underscore the demand for fair and privacy-preserving FL schemes. Here, collaborative fairness asks for individual model quality depending on the individual data contribution. Privacy is demanded with respect to any kind of data outsourced to the third party. Now, there already exist some approaches aiming for either fair or privacy-preserving FL and a few works even address both features. In our paper, we build upon these seminal works and present a novel, fair and privacy-preserving FL scheme. Our approach, which mainly relies on homomorphic encryption, stands out for exclusively using local gradients. This increases the usability in comparison to state-of-the-art approaches and thereby opens the door to applications in control.", "authors": ["Janis Adamek", "Moritz Schulze Darup"], "categories": ["cs.LG", "cs.CR", "eess.SY"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-07-18", "url": "https://arxiv.org/abs/2407.13881", "pdf_url": "https://arxiv.org/pdf/2407.13881v1", "arxiv_id": "2407.13881", "doi": "10.1109/CoDIT62066.2024.10708141", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Control, Decision and Information Technologies", "quality_score": 0.1193}
{"id": "76f475c3166914f56c84addf5def2fcf1c0a03b4aec698eee711bf8cb505e3e5", "sources": ["arxiv", "semantic_scholar"], "title": "Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing", "abstract": "Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles' local models instead of local data. The gradients of vehicles' local models are usually large for the vehicular artificial intelligence (AI) applications, thus transmitting such large gradients would cause large per-round latency. Gradient quantization has been proposed as one effective approach to reduce the per-round latency in FL enabled VEC through compressing gradients and reducing the number of bits, i.e., the quantization level, to transmit gradients. The selection of quantization level and thresholds determines the quantization error, which further affects the model accuracy and training time. To do so, the total training time and quantization error (QE) become two key metrics for the FL enabled VEC. It is critical to jointly optimize the total training time and QE for the FL enabled VEC. However, the time-varying channel condition causes more challenges to solve this problem. In this paper, we propose a distributed deep reinforcement learning (DRL)-based quantization level allocation scheme to optimize the long-term reward in terms of the total training time and QE. Extensive simulations identify the optimal weighted factors between the total training time and QE, and demonstrate the feasibility and effectiveness of the proposed scheme.", "authors": ["Cui Zhang", "Wenjun Zhang", "Qiong Wu", "Pingyi Fan", "Qiang Fan", "Jiangzhou Wang", "Khaled B. Letaief"], "categories": ["cs.LG", "cs.NI"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-11", "url": "https://arxiv.org/abs/2407.08462", "pdf_url": "https://arxiv.org/pdf/2407.08462v2", "arxiv_id": "2407.08462", "doi": "10.1109/JIOT.2024.3447036", "citation_count": 93, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/qiongwu86/Distributed-Deep-Reinforcement-Learning-Based-Gradient", "venue": "IEEE Internet of Things Journal", "quality_score": 0.4933}
{"id": "a785f72c7f8b503a90f8390bc0723ec13d831a192c201bb9a6875a806376e83d", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models (Extended Version)", "abstract": "Deduplication is a vital preprocessing step that enhances machine learning model performance and saves training time and energy. However, enhancing federated learning through deduplication poses challenges, especially regarding scalability and potential privacy violations if deduplication involves sharing all clients' data. In this paper, we address the problem of deduplication in a federated setup by introducing a pioneering protocol, Efficient Privacy-Preserving Multi-Party Deduplication (EP-MPD). It efficiently removes duplicates from multiple clients' datasets without compromising data privacy. EP-MPD is constructed in a modular fashion, utilizing two novel variants of the Private Set Intersection protocol. Our extensive experiments demonstrate the significant benefits of deduplication in federated learning of large language models. For instance, we observe up to 19.62\\% improvement in perplexity and up to 27.95\\% reduction in running time while varying the duplication level between 10\\% and 30\\%. EP-MPD effectively balances privacy and performance in federated learning, making it a valuable solution for large-scale applications.", "authors": ["Aydin Abadi", "Vishnu Asutosh Dasu", "Sumanta Sarkar"], "categories": ["cs.CR", "cs.AI", "cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-11", "url": "https://arxiv.org/abs/2407.08152", "pdf_url": "https://arxiv.org/pdf/2407.08152v2", "arxiv_id": "2407.08152", "doi": "10.48550/arXiv.2407.08152", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IACR Cryptology ePrint Archive", "quality_score": 0.2113}
{"id": "f8fc65ce496a774267d11a6f30f1a5bd5b184bc27aad819537baf93f946840ce", "sources": ["arxiv", "semantic_scholar"], "title": "Collection, usage and privacy of mobility data in the enterprise and public administrations", "abstract": "Human mobility data is a crucial resource for urban mobility management, but it does not come without personal reference. The implementation of security measures such as anonymization is thus needed to protect individuals' privacy. Often, a trade-off arises as such techniques potentially decrease the utility of the data and limit its use. While much research on anonymization techniques exists, there is little information on the actual implementations by practitioners, especially outside the big tech context. Within our study, we conducted expert interviews to gain insights into practices in the field. We categorize purposes, data sources, analysis, and modeling tasks to provide a profound understanding of the context such data is used in. We survey privacy-enhancing methods in use, which generally do not comply with state-of-the-art standards of differential privacy. We provide groundwork for further research on practice-oriented research by identifying privacy needs of practitioners and extracting relevant mobility characteristics for future standardized evaluations of privacy-enhancing methods.", "authors": ["Alexandra Kapp"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-04", "url": "https://arxiv.org/abs/2407.03732", "pdf_url": "https://arxiv.org/pdf/2407.03732v1", "arxiv_id": "2407.03732", "doi": "10.56553/popets-2022-0117", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Proceedings on Privacy Enhancing Technologies", "quality_score": 0.2386}
{"id": "13ae3da2dc9c8e95755d04434c955db908dfa4a047b8ddabf9e4a164be5c4d1a", "sources": ["arxiv", "semantic_scholar"], "title": "Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning", "abstract": "With the rapid development of intelligent vehicles and Intelligent Transport Systems (ITS), the sensors such as cameras and LiDAR installed on intelligent vehicles provides higher capacity of executing computation-intensive and delay-sensitive tasks, thereby raising deployment costs. To address this issue, Vehicular Edge Computing (VEC) has been proposed to process data through Road Side Units (RSUs) to support real-time applications. This paper focuses on the Age of Information (AoI) as a key metric for data freshness and explores task offloading issues for vehicles under RSU communication resource constraints. We adopt a Multi-agent Deep Reinforcement Learning (MADRL) approach, allowing vehicles to autonomously make optimal data offloading decisions. However, MADRL poses risks of vehicle information leakage during communication learning and centralized training. To mitigate this, we employ a Federated Learning (FL) framework that shares model parameters instead of raw data to protect the privacy of vehicle users. Building on this, we propose an innovative distributed federated learning framework combining Graph Neural Networks (GNN), named Federated Graph Neural Network Multi-Agent Reinforcement Learning (FGNN-MADRL), to optimize AoI across the system. For the first time, road scenarios are constructed as graph data structures, and a GNN-based federated learning framework is proposed, effectively combining distributed and centralized federated aggregation. Furthermore, we propose a new MADRL algorithm that simplifies decision making and enhances offloading efficiency, further reducing the decision complexity. Simulation results demonstrate the superiority of our proposed approach to other methods through simulations.", "authors": ["Wenhua Wang", "Qiong Wu", "Pingyi Fan", "Nan Cheng", "Wen Chen", "Jiangzhou Wang", "Khaled B. Letaief"], "categories": ["cs.LG", "cs.DC", "cs.MA", "cs.NI"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-01", "url": "https://arxiv.org/abs/2407.02342", "pdf_url": "https://arxiv.org/pdf/2407.02342v1", "arxiv_id": "2407.02342", "doi": "10.48550/arXiv.2407.02342", "citation_count": 8, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/qiongwu86/Optimizing-AoI-in-VEC-with-Federated-Graph-Neural-Network-Multi-Agent-Reinforcement-Learning", "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "6c6102b40489852f77fbb7ad6474d892584aa7d75fea2e0ad1c96c48c7faa616", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy Preserving Machine Learning for Electronic Health Records using Federated Learning and Differential Privacy", "abstract": "An Electronic Health Record (EHR) is an electronic database used by healthcare providers to store patients' medical records which may include diagnoses, treatments, costs, and other personal information. Machine learning (ML) algorithms can be used to extract and analyze patient data to improve patient care. Patient records contain highly sensitive information, such as social security numbers (SSNs) and residential addresses, which introduces a need to apply privacy-preserving techniques for these ML models using federated learning and differential privacy.", "authors": ["Naif A. Ganadily", "Han J. Xia"], "categories": ["cs.LG", "cs.CR", "cs.ET"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-23", "url": "https://arxiv.org/abs/2406.15962", "pdf_url": "https://arxiv.org/pdf/2406.15962v1", "arxiv_id": "2406.15962", "doi": "10.48550/arXiv.2406.15962", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "a07bc6cf64196611595ff6875776f6c1b8944d9eaefad22d7d70ded9140ae76e", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation", "abstract": "Machine learning (ML) and Artificial Intelligence (AI) have fueled remarkable advancements, particularly in healthcare. Within medical imaging, ML models hold the promise of improving disease diagnoses, treatment planning, and post-treatment monitoring. Various computer vision tasks like image classification, object detection, and image segmentation are poised to become routine in clinical analysis. However, privacy concerns surrounding patient data hinder the assembly of large training datasets needed for developing and training accurate, robust, and generalizable models. Federated Learning (FL) emerges as a compelling solution, enabling organizations to collaborate on ML model training by sharing model training information (gradients) rather than data (e.g., medical images). FL's distributed learning framework facilitates inter-institutional collaboration while preserving patient privacy. However, FL, while robust in privacy preservation, faces several challenges. Sensitive information can still be gleaned from shared gradients that are passed on between organizations during model training. Additionally, in medical imaging, quantifying model confidence\\uncertainty accurately is crucial due to the noise and artifacts present in the data. Uncertainty estimation in FL encounters unique hurdles due to data heterogeneity across organizations. This paper offers a comprehensive review of FL, privacy preservation, and uncertainty estimation, with a focus on medical imaging. Alongside a survey of current research, we identify gaps in the field and suggest future directions for FL research to enhance privacy and address noisy medical imaging data challenges.", "authors": ["Nikolas Koutsoubis", "Yasin Yilmaz", "Ravi P. Ramachandran", "Matthew Schabath", "Ghulam Rasool"], "categories": ["cs.LG", "cs.AI", "cs.DC", "eess.IV", "stat.ML"], "fields_of_study": ["Computer Science", "Engineering", "Mathematics"], "published_date": "2024-06-18", "url": "https://arxiv.org/abs/2406.12815", "pdf_url": "https://arxiv.org/pdf/2406.12815v1", "arxiv_id": "2406.12815", "doi": "10.48550/arXiv.2406.12815", "citation_count": 26, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3578}
{"id": "4d4adaf0fea459330849e892166531c86068e8ac44fb8dcf59fbd077e99b2577", "sources": ["arxiv", "semantic_scholar"], "title": "Promoting Data and Model Privacy in Federated Learning through Quantized LoRA", "abstract": "Conventional federated learning primarily aims to secure the privacy of data distributed across multiple edge devices, with the global model dispatched to edge devices for parameter updates during the learning process. However, the development of large language models (LLMs) requires substantial data and computational resources, rendering them valuable intellectual properties for their developers and owners. To establish a mechanism that protects both data and model privacy in a federated learning context, we introduce a method that just needs to distribute a quantized version of the model's parameters during training. This method enables accurate gradient estimations for parameter updates while preventing clients from accessing a model whose performance is comparable to the centrally hosted one. Moreover, we combine this quantization strategy with LoRA, a popular and parameter-efficient fine-tuning method, to significantly reduce communication costs in federated learning. The proposed framework, named \\textsc{FedLPP}, successfully ensures both data and model privacy in the federated learning context. Additionally, the learned central model exhibits good generalization and can be trained in a resource-efficient manner.", "authors": ["JianHao Zhu", "Changze Lv", "Xiaohua Wang", "Muling Wu", "Wenhao Liu", "Tianlong Li", "Zixuan Ling", "Cenyuan Zhang", "Xiaoqing Zheng", "Xuanjing Huang"], "categories": ["cs.LG", "cs.CL", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-16", "url": "https://arxiv.org/abs/2406.10976", "pdf_url": "https://arxiv.org/pdf/2406.10976v1", "arxiv_id": "2406.10976", "doi": "10.48550/arXiv.2406.10976", "citation_count": 14, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.294}
{"id": "1282ee9230196185d57ad379f5af61cea0fe36d3fa8e3b2518f0cabbf611f7fa", "sources": ["arxiv", "semantic_scholar"], "title": "A deep cut into Split Federated Self-supervised Learning", "abstract": "Collaborative self-supervised learning has recently become feasible in highly distributed environments by dividing the network layers between client devices and a central server. However, state-of-the-art methods, such as MocoSFL, are optimized for network division at the initial layers, which decreases the protection of the client data and increases communication overhead. In this paper, we demonstrate that splitting depth is crucial for maintaining privacy and communication efficiency in distributed training. We also show that MocoSFL suffers from a catastrophic quality deterioration for the minimal communication overhead. As a remedy, we introduce Momentum-Aligned contrastive Split Federated Learning (MonAcoSFL), which aligns online and momentum client models during training procedure. Consequently, we achieve state-of-the-art accuracy while significantly reducing the communication overhead, making MonAcoSFL more practical in real-world scenarios.", "authors": ["Marcin Przewięźlikowski", "Marcin Osial", "Bartosz Zieliński", "Marek Śmieja"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-12", "url": "https://arxiv.org/abs/2406.08267", "pdf_url": "https://arxiv.org/pdf/2406.08267v2", "arxiv_id": "2406.08267", "doi": "10.1007/978-3-031-70344-7_26", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2024. Lecture Notes in Computer Science, vol 14942. Springer, Cham", "quality_score": 0.0753}
{"id": "3a04a9d592df7ac97428db9351b5cde08779bfb3992ecf832e79c407aa534220", "sources": ["arxiv", "semantic_scholar"], "title": "Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints", "abstract": "This paper studies federated learning for nonparametric regression in the context of distributed samples across different servers, each adhering to distinct differential privacy constraints. The setting we consider is heterogeneous, encompassing both varying sample sizes and differential privacy constraints across servers. Within this framework, both global and pointwise estimation are considered, and optimal rates of convergence over the Besov spaces are established. Distributed privacy-preserving estimators are proposed and their risk properties are investigated. Matching minimax lower bounds, up to a logarithmic factor, are established for both global and pointwise estimation. Together, these findings shed light on the tradeoff between statistical accuracy and privacy preservation. In particular, we characterize the compromise not only in terms of the privacy budget but also concerning the loss incurred by distributing data within the privacy framework as a whole. This insight captures the folklore wisdom that it is easier to retain privacy in larger samples, and explores the differences between pointwise and global estimation under distributed privacy constraints.", "authors": ["T. Tony Cai", "Abhinav Chakraborty", "Lasse Vuursteen"], "categories": ["math.ST", "cs.LG", "stat.ML"], "fields_of_study": ["Mathematics", "Computer Science"], "published_date": "2024-06-10", "url": "https://arxiv.org/abs/2406.06755", "pdf_url": "https://arxiv.org/pdf/2406.06755v1", "arxiv_id": "2406.06755", "doi": "10.48550/arXiv.2406.06755", "citation_count": 13, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2865}
{"id": "46c45f3dc3d235098bd289f55f6883b13042f2620937f12b483073961fcbc28f", "sources": ["arxiv", "semantic_scholar"], "title": "Parameterizing Federated Continual Learning for Reproducible Research", "abstract": "Federated Learning (FL) systems evolve in heterogeneous and ever-evolving environments that challenge their performance. Under real deployments, the learning tasks of clients can also evolve with time, which calls for the integration of methodologies such as Continual Learning. To enable research reproducibility, we propose a set of experimental best practices that precisely capture and emulate complex learning scenarios. Our framework, Freddie, is the first entirely configurable framework for Federated Continual Learning (FCL), and it can be seamlessly deployed on a large number of machines thanks to the use of Kubernetes and containerization. We demonstrate the effectiveness of Freddie on two use cases, (i) large-scale FL on CIFAR100 and (ii) heterogeneous task sequence on FCL, which highlight unaddressed performance challenges in FCL scenarios.", "authors": ["Bart Cox", "Jeroen Galjaard", "Aditya Shankar", "Jérémie Decouchant", "Lydia Y. Chen"], "categories": ["cs.LG", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-04", "url": "https://arxiv.org/abs/2406.02015", "pdf_url": "https://arxiv.org/pdf/2406.02015v1", "arxiv_id": "2406.02015", "doi": "10.48550/arXiv.2406.02015", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0753}
{"id": "442af14978e28e9f7f2126250fc87c254d819aa6a84d703f1936ee4476ebbd5a", "sources": ["arxiv", "semantic_scholar"], "title": "No Vandalism: Privacy-Preserving and Byzantine-Robust Federated Learning", "abstract": "Federated learning allows several clients to train one machine learning model jointly without sharing private data, providing privacy protection. However, traditional federated learning is vulnerable to poisoning attacks, which can not only decrease the model performance, but also implant malicious backdoors. In addition, direct submission of local model parameters can also lead to the privacy leakage of the training dataset. In this paper, we aim to build a privacy-preserving and Byzantine-robust federated learning scheme to provide an environment with no vandalism (NoV) against attacks from malicious participants. Specifically, we construct a model filter for poisoned local models, protecting the global model from data and model poisoning attacks. This model filter combines zero-knowledge proofs to provide further privacy protection. Then, we adopt secret sharing to provide verifiable secure aggregation, removing malicious clients that disrupting the aggregation process. Our formal analysis proves that NoV can protect data privacy and weed out Byzantine attackers. Our experiments illustrate that NoV can effectively address data and model poisoning attacks, including PGD, and outperforms other related schemes.", "authors": ["Zhibo Xing", "Zijian Zhang", "Zi'ang Zhang", "Jiamou Liu", "Liehuang Zhu", "Giovanni Russello"], "categories": ["cs.CR", "cs.DC", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-03", "url": "https://arxiv.org/abs/2406.01080", "pdf_url": "https://arxiv.org/pdf/2406.01080v1", "arxiv_id": "2406.01080", "doi": "10.48550/arXiv.2406.01080", "citation_count": 6, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2113}
{"id": "49db7920d72abb78c3198b975dac4152a51eefd1ec2826f1a8be97b6c9844939", "sources": ["arxiv", "semantic_scholar"], "title": "FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation", "abstract": "Federated learning (FL) has emerged as a collaborative approach that allows multiple clients to jointly learn a machine learning model without sharing their private data. The concern about privacy leakage, albeit demonstrated under specific conditions, has triggered numerous follow-up research in designing powerful attacking methods and effective defending mechanisms aiming to thwart these attacking methods. Nevertheless, privacy-preserving mechanisms employed in these defending methods invariably lead to compromised model performances due to a fixed obfuscation applied to private data or gradients. In this article, we, therefore, propose a novel adaptive obfuscation mechanism, coined FedAdOb, to protect private data without yielding original model performances. Technically, FedAdOb utilizes passport-based adaptive obfuscation to ensure data privacy in both horizontal and vertical federated learning settings. The privacy-preserving capabilities of FedAdOb, specifically with regard to private features and labels, are theoretically proven through Theorems 1 and 2. Furthermore, extensive experimental evaluations conducted on various datasets and network architectures demonstrate the effectiveness of FedAdOb by manifesting its superior trade-off between privacy preservation and model performance, surpassing existing methods.", "authors": ["Hanlin Gu", "Jiahuan Luo", "Yan Kang", "Yuan Yao", "Gongxi Zhu", "Bowen Li", "Lixin Fan", "Qiang Yang"], "categories": ["cs.CR", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-03", "url": "https://arxiv.org/abs/2406.01085", "pdf_url": "https://arxiv.org/pdf/2406.01085v1", "arxiv_id": "2406.01085", "doi": "10.48550/arXiv.2406.01085", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "6592831b08c299c49e709b9d5bd7b18d102074926c94ada9af20726b47fb0ae8", "sources": ["arxiv", "semantic_scholar"], "title": "Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and Privacy", "abstract": "In this paper, we propose feature-based federated transfer learning as a novel approach to improve communication efficiency by reducing the uplink payload by multiple orders of magnitude compared to that of existing approaches in federated learning and federated transfer learning. Specifically, in the proposed feature-based federated learning, we design the extracted features and outputs to be uploaded instead of parameter updates. For this distributed learning model, we determine the required payload and provide comparisons with the existing schemes. Subsequently, we analyze the robustness of feature-based federated transfer learning against packet loss, data insufficiency, and quantization. Finally, we address privacy considerations by defining and analyzing label privacy leakage and feature privacy leakage, and investigating mitigating approaches. For all aforementioned analyses, we evaluate the performance of the proposed learning scheme via experiments on an image classification task and a natural language processing task to demonstrate its effectiveness.", "authors": ["Feng Wang", "M. Cenk Gursoy", "Senem Velipasalar"], "categories": ["cs.LG", "cs.MA"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-15", "url": "https://arxiv.org/abs/2405.09014", "pdf_url": "https://arxiv.org/pdf/2405.09014v1", "arxiv_id": "2405.09014", "doi": "10.1109/TMLCN.2024.3408131", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Machine Learning in Communications and Networking", "quality_score": 0.1945}
{"id": "21000fec970cd7b001f7b29cac5020d6f3d665bbec733d0e4709418022d1a046", "sources": ["arxiv", "semantic_scholar"], "title": "SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learning", "abstract": "In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient communication. SSFL identifies a sparse subnetwork prior to training, leveraging parameter saliency scores computed separately on local client data in non-IID scenarios, and then aggregated, to determine a global mask. Only the sparse model weights are trained and communicated each round between the clients and the server. On standard benchmarks including CIFAR-10, CIFAR-100, and Tiny-ImageNet, SSFL consistently improves the accuracy sparsity trade off, achieving more than 20\\% relative error reduction on CIFAR-10 compared to the strongest sparse baseline, while reducing communication costs by $2 \\times$ relative to dense FL. Finally, in a real-world federated learning deployment, SSFL delivers over $2.3 \\times$ faster communication time, underscoring its practical efficiency.", "authors": ["Riyasat Ohib", "Bishal Thapaliya", "Gintare Karolina Dziugaite", "Jingyu Liu", "Vince Calhoun", "Sergey Plis"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-15", "url": "https://arxiv.org/abs/2405.09037", "pdf_url": "https://arxiv.org/pdf/2405.09037v2", "arxiv_id": "2405.09037", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Transactions on Machine Learning Research, 2026", "quality_score": 0.0753}
{"id": "ca6ec05114bca7ed2fdc484c548b2d8b2b20108571f35fed2dd64e86e0eef6c2", "sources": ["arxiv", "semantic_scholar"], "title": "Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data", "abstract": "The proliferation of edge devices has brought Federated Learning (FL) to the forefront as a promising paradigm for decentralized and collaborative model training while preserving the privacy of clients' data. However, FL struggles with a significant performance reduction and poor convergence when confronted with Non-Independent and Identically Distributed (Non-IID) data distributions among participating clients. While previous efforts, such as client drift mitigation and advanced server-side model fusion techniques, have shown some success in addressing this challenge, they often overlook the root cause of the performance reduction - the absence of identical data accurately mirroring the global data distribution among clients. In this paper, we introduce Gen-FedSD, a novel approach that harnesses the powerful capability of state-of-the-art text-to-image foundation models to bridge the significant Non-IID performance gaps in FL. In Gen-FedSD, each client constructs textual prompts for each class label and leverages an off-the-shelf state-of-the-art pre-trained Stable Diffusion model to synthesize high-quality data samples. The generated synthetic data is tailored to each client's unique local data gaps and distribution disparities, effectively making the final augmented local data IID. Through extensive experimentation, we demonstrate that Gen-FedSD achieves state-of-the-art performance and significant communication cost savings across various datasets and Non-IID settings.", "authors": ["Mahdi Morafah", "Matthias Reisser", "Bill Lin", "Christos Louizos"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-13", "url": "https://arxiv.org/abs/2405.07925", "pdf_url": "https://arxiv.org/pdf/2405.07925v1", "arxiv_id": "2405.07925", "doi": "10.48550/arXiv.2405.07925", "citation_count": 13, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2865}
{"id": "74ea6cf2f9b91858de48290e5a68516fdc8fbdf0c154684bac5599a807da0397", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systems", "abstract": "Machine Learning (ML) algorithms are generally designed for scenarios in which all data is stored in one data center, where the training is performed. However, in many applications, e.g., in the healthcare domain, the training data is distributed among several entities, e.g., different hospitals or patients' mobile devices/sensors. At the same time, transferring the data to a central location for learning is certainly not an option, due to privacy concerns and legal issues, and in certain cases, because of the communication and computation overheads. Federated Learning (FL) is the state-of-the-art collaborative ML approach for training an ML model across multiple parties holding local data samples, without sharing them. However, enabling learning from distributed data over such edge Internet of Things (IoT) systems (e.g., mobile-health and wearable technologies, involving sensitive personal/medical data) in a privacy-preserving fashion presents a major challenge mainly due to their stringent resource constraints, i.e., limited computing capacity, communication bandwidth, memory storage, and battery lifetime. In this paper, we propose a privacy-preserving edge FL framework for resource-constrained mobile-health and wearable technologies over the IoT infrastructure. We evaluate our proposed framework extensively and provide the implementation of our technique on Amazon's AWS cloud platform based on the seizure detection application in epilepsy monitoring using wearable technologies.", "authors": ["Amin Aminifar", "Matin Shokri", "Amir Aminifar"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-09", "url": "https://arxiv.org/abs/2405.05611", "pdf_url": "https://arxiv.org/pdf/2405.05611v2", "arxiv_id": "2405.05611", "doi": "10.1016/j.future.2024.07.035", "citation_count": 71, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "Future generations computer systems", "quality_score": 0.4643}
{"id": "86b40b9573bfdb002098ff52d2d9877a07a90f04f30706c09a407c54e3b6f698", "sources": ["arxiv", "semantic_scholar"], "title": "Quantum Federated Learning Experiments in the Cloud with Data Encoding", "abstract": "Quantum Federated Learning (QFL) is an emerging concept that aims to unfold federated learning (FL) over quantum networks, enabling collaborative quantum model training along with local data privacy. We explore the challenges of deploying QFL on cloud platforms, emphasizing quantum intricacies and platform limitations. The proposed data-encoding-driven QFL, with a proof of concept (GitHub Open Source) using genomic data sets on quantum simulators, shows promising results.", "authors": ["Shiva Raj Pokhrel", "Naman Yash", "Jonathan Kua", "Gang Li", "Lei Pan"], "categories": ["cs.LG", "cs.ET", "quant-ph"], "fields_of_study": ["Computer Science", "Physics"], "published_date": "2024-05-01", "url": "https://arxiv.org/abs/2405.00909", "pdf_url": "https://arxiv.org/pdf/2405.00909v1", "arxiv_id": "2405.00909", "doi": "10.48550/arXiv.2405.00909", "citation_count": 13, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2865}
{"id": "9351a8d76b70331868b4ff7f3bbd2c03aa3d9838f25d242c1f866e27e495b7d5", "sources": ["arxiv", "semantic_scholar"], "title": "Communication-Efficient Training Workload Balancing for Decentralized Multi-Agent Learning", "abstract": "Decentralized Multi-agent Learning (DML) enables collaborative model training while preserving data privacy. However, inherent heterogeneity in agents' resources (computation, communication, and task size) may lead to substantial variations in training time. This heterogeneity creates a bottleneck, lengthening the overall training time due to straggler effects and potentially wasting spare resources of faster agents. To minimize training time in heterogeneous environments, we present a Communication-Efficient Training Workload Balancing for Decentralized Multi-Agent Learning (ComDML), which balances the workload among agents through a decentralized approach. Leveraging local-loss split training, ComDML enables parallel updates, where slower agents offload part of their workload to faster agents. To minimize the overall training time, ComDML optimizes the workload balancing by jointly considering the communication and computation capacities of agents, which hinges upon integer programming. A dynamic decentralized pairing scheduler is developed to efficiently pair agents and determine optimal offloading amounts. We prove that in ComDML, both slower and faster agents' models converge, for convex and non-convex functions. Furthermore, extensive experimental results on popular datasets (CIFAR-10, CIFAR-100, and CINIC-10) and their non-I.I.D. variants, with large models such as ResNet-56 and ResNet-110, demonstrate that ComDML can significantly reduce the overall training time while maintaining model accuracy, compared to state-of-the-art methods. ComDML demonstrates robustness in heterogeneous environments, and privacy measures can be seamlessly integrated for enhanced data protection.", "authors": ["Seyed Mahmoud Sajjadi Mohammadabadi", "Lei Yang", "Feng Yan", "Junshan Zhang"], "categories": ["cs.LG", "cs.AI", "cs.DC", "cs.MA", "cs.PF"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-01", "url": "https://arxiv.org/abs/2405.00839", "pdf_url": "https://arxiv.org/pdf/2405.00839v1", "arxiv_id": "2405.00839", "doi": "10.1109/ICDCS60910.2024.00069", "citation_count": 19, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Distributed Computing Systems", "quality_score": 0.3253}
{"id": "f108924797a8007acb38d5f97b2e106c95ef1bc5b941f4aeea91c6eb4622c605", "sources": ["arxiv", "semantic_scholar"], "title": "Harnessing Federated Generative Learning for Green and Sustainable Internet of Things", "abstract": "The rapid proliferation of devices in the Internet of Things (IoT) has ushered in a transformative era of data-driven connectivity across various domains. However, this exponential growth has raised pressing concerns about environmental sustainability and data privacy. In response to these challenges, this paper introduces One-shot Federated Learning (OSFL), an innovative paradigm that harmonizes sustainability and machine learning within IoT ecosystems. OSFL revolutionizes the traditional Federated Learning (FL) workflow by condensing multiple iterative communication rounds into a single operation, thus significantly reducing energy consumption, communication overhead, and latency. This breakthrough is coupled with the strategic integration of generative learning techniques, ensuring robust data privacy while promoting efficient knowledge sharing among IoT devices. By curtailing resource utilization, OSFL aligns seamlessly with the vision of green and sustainable IoT, effectively extending device lifespans and mitigating their environmental footprint. Our research underscores the transformative potential of OSFL, poised to reshape the landscape of IoT applications across domains such as energy-efficient smart cities and groundbreaking healthcare solutions. This contribution marks a pivotal step towards a more responsible, sustainable, and technologically advanced future.", "authors": ["Yuanhang Qi", "M. Shamim Hossain"], "categories": ["cs.NI", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-30", "url": "https://arxiv.org/abs/2407.05915", "pdf_url": "https://arxiv.org/pdf/2407.05915v1", "arxiv_id": "2407.05915", "doi": "10.1016/j.jnca.2023.103812", "citation_count": 16, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Journal of Network and Computer Applications", "quality_score": 0.3076}
{"id": "b0df9fbba38d3cdc1acec350c50c90f83a189760f5837704173a1db8e79742b1", "sources": ["arxiv", "semantic_scholar"], "title": "Secure and Privacy-Preserving Authentication for Data Subject Rights Enforcement", "abstract": "In light of the GDPR, data controllers (DC) need to allow data subjects (DS) to exercise certain data subject rights. A key requirement here is that DCs can reliably authenticate a DS. Due to a lack of clear technical specifications, this has been realized in different ways, such as by requesting copies of ID documents or by email address verification. However, previous research has shown that this is associated with various security and privacy risks and that identifying DSs can be a non-trivial task. In this paper, we review different authentication schemes and propose an architecture that enables DCs to authenticate DSs with the help of independent Identity Providers in a secure and privacy-preserving manner by utilizing attribute-based credentials and eIDs. Our work contributes to a more standardized and privacy-preserving way of authenticating DSs, which will benefit both DCs and DSs.", "authors": ["Malte Hansen", "Andre Büttner"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-24", "url": "https://arxiv.org/abs/2404.15859", "pdf_url": "https://arxiv.org/pdf/2404.15859v1", "arxiv_id": "2404.15859", "doi": "10.1007/978-3-031-57978-3_12", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Privacy and Identity Management. Sharing in a Digital World. Privacy and Identity 2023. IFIP Advances in Information and Communication Technology, vol 695. Springer, Cham", "quality_score": 0.0}
{"id": "277a1bda802560883ce36ad7e2bf1f0e9ec44745c586a5b6a73bbf76e2c4333c", "sources": ["arxiv", "semantic_scholar"], "title": "A Federated Learning Approach to Privacy Preserving Offensive Language Identification", "abstract": "The spread of various forms of offensive speech online is an important concern in social media. While platforms have been investing heavily in ways of coping with this problem, the question of privacy remains largely unaddressed. Models trained to detect offensive language on social media are trained and/or fine-tuned using large amounts of data often stored in centralized servers. Since most social media data originates from end users, we propose a privacy preserving decentralized architecture for identifying offensive language online by introducing Federated Learning (FL) in the context of offensive language identification. FL is a decentralized architecture that allows multiple models to be trained locally without the need for data sharing hence preserving users' privacy. We propose a model fusion approach to perform FL. We trained multiple deep learning models on four publicly available English benchmark datasets (AHSD, HASOC, HateXplain, OLID) and evaluated their performance in detail. We also present initial cross-lingual experiments in English and Spanish. We show that the proposed model fusion approach outperforms baselines in all the datasets while preserving privacy.", "authors": ["Marcos Zampieri", "Damith Premasiri", "Tharindu Ranasinghe"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-17", "url": "https://arxiv.org/abs/2404.11470", "pdf_url": "https://arxiv.org/pdf/2404.11470v1", "arxiv_id": "2404.11470", "doi": "10.48550/arXiv.2404.11470", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Workshop on Trolling, Aggression and Cyberbullying", "quality_score": 0.2113}
{"id": "d5bbf20922a7c05fa10172ea408442b7ee5e581cf37afe1db720d01a38333810", "sources": ["arxiv", "semantic_scholar"], "title": "Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detection", "abstract": "Distributed training can facilitate the processing of large medical image datasets, and improve the accuracy and efficiency of disease diagnosis while protecting patient privacy, which is crucial for achieving efficient medical image analysis and accelerating medical research progress. This paper presents an innovative approach to medical image classification, leveraging Federated Learning (FL) to address the dual challenges of data privacy and efficient disease diagnosis. Traditional Centralized Machine Learning models, despite their widespread use in medical imaging for tasks such as disease diagnosis, raise significant privacy concerns due to the sensitive nature of patient data. As an alternative, FL emerges as a promising solution by allowing the training of a collective global model across local clients without centralizing the data, thus preserving privacy. Focusing on the application of FL in Magnetic Resonance Imaging (MRI) brain tumor detection, this study demonstrates the effectiveness of the Federated Learning framework coupled with EfficientNet-B0 and the FedAvg algorithm in enhancing both privacy and diagnostic accuracy. Through a meticulous selection of preprocessing methods, algorithms, and hyperparameters, and a comparative analysis of various Convolutional Neural Network (CNN) architectures, the research uncovers optimal strategies for image classification. The experimental results reveal that EfficientNet-B0 outperforms other models like ResNet in handling data heterogeneity and achieving higher accuracy and lower loss, highlighting the potential of FL in overcoming the limitations of traditional models. The study underscores the significance of addressing data heterogeneity and proposes further research directions for broadening the applicability of FL in medical image analysis.", "authors": ["Lisang Zhou", "Meng Wang", "Ning Zhou"], "categories": ["eess.IV", "cs.CR", "cs.LG"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2024-04-15", "url": "https://arxiv.org/abs/2404.10026", "pdf_url": "https://arxiv.org/pdf/2404.10026v1", "arxiv_id": "2404.10026", "doi": "10.48550/arXiv.2404.10026", "citation_count": 70, "influential_citation_count": 6, "has_code": false, "code_url": null, "venue": "Journal of Information, Technology and Policy (2023): 1-12", "quality_score": 0.4628}
{"id": "50144da288ff12f54a0aa8f973f6fc23bda0c9dd74cf4833f8176194d726f59b", "sources": ["arxiv", "semantic_scholar"], "title": "On the Efficiency of Privacy Attacks in Federated Learning", "abstract": "Recent studies have revealed severe privacy risks in federated learning, represented by Gradient Leakage Attacks. However, existing studies mainly aim at increasing the privacy attack success rate and overlook the high computation costs for recovering private data, making the privacy attack impractical in real applications. In this study, we examine privacy attacks from the perspective of efficiency and propose a framework for improving the Efficiency of Privacy Attacks in Federated Learning (EPAFL). We make three novel contributions. First, we systematically evaluate the computational costs for representative privacy attacks in federated learning, which exhibits a high potential to optimize efficiency. Second, we propose three early-stopping techniques to effectively reduce the computational costs of these privacy attacks. Third, we perform experiments on benchmark datasets and show that our proposed method can significantly reduce computational costs and maintain comparable attack success rates for state-of-the-art privacy attacks in federated learning. We provide the codes on GitHub at https://github.com/mlsysx/EPAFL.", "authors": ["Nawrin Tabassum", "Ka-Ho Chow", "Xuyu Wang", "Wenbin Zhang", "Yanzhao Wu"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-15", "url": "https://arxiv.org/abs/2404.09430", "pdf_url": "https://arxiv.org/pdf/2404.09430v1", "arxiv_id": "2404.09430", "doi": "10.1109/CVPRW63382.2024.00426", "citation_count": 5, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/mlsysx/EPAFL", "venue": null, "quality_score": 0.1945}
{"id": "9cae3d88647b188ee3f98f800c7996d8108f28f771a06d95fabd0871f2e4afee", "sources": ["arxiv", "semantic_scholar"], "title": "Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning", "abstract": "While centralized servers pose a risk of being a single point of failure, decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Merging distributed computing with cryptographic techniques, decentralized technologies introduce a novel computing paradigm. Blockchain ensures secure, transparent, and tamper-proof data management by validating and recording transactions via consensus across network nodes. Federated Learning (FL), as a distributed machine learning framework, enables participants to collaboratively train models while safeguarding data privacy by avoiding direct raw data exchange. Despite the growing interest in decentralized methods, their application in FL remains underexplored. This paper presents a thorough investigation into Blockchain-based FL (BCFL), spotlighting the synergy between blockchain's security features and FL's privacy-preserving model training capabilities. First, we present the taxonomy of BCFL from three aspects, including decentralized, separate networks, and reputation-based architectures. Then, we summarize the general architecture of BCFL systems, providing a comprehensive perspective on FL architectures informed by blockchain. Afterward, we analyze the application of BCFL in healthcare, IoT, and other privacy-sensitive areas. Finally, we identify future research directions of BCFL.", "authors": ["Ji Liu", "Chunlu Chen", "Yu Li", "Lin Sun", "Yulun Song", "Jingbo Zhou", "Bo Jing", "Dejing Dou"], "categories": ["cs.CR", "cs.AI", "cs.DC", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-28", "url": "https://arxiv.org/abs/2403.19178", "pdf_url": "https://arxiv.org/pdf/2403.19178v1", "arxiv_id": "2403.19178", "doi": "10.1007/s10115-024-02117-3", "citation_count": 49, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "Knowledge and Information Systems", "quality_score": 0.4247}
{"id": "bb6a99b66b6363ddb64e6e59ee0c6bfc7908e589dc846f94a4929b43ef46fd82", "sources": ["arxiv", "semantic_scholar"], "title": "Enhancing Privacy in Federated Learning through Local Training", "abstract": "In this paper we propose the federated learning algorithm Fed-PLT to overcome the challenges of (i) expensive communications and (ii) privacy preservation. We address (i) by allowing for both partial participation and local training, which significantly reduce the number of communication rounds between the central coordinator and computing agents. The algorithm matches the state of the art in the sense that the use of local training demonstrably does not impact accuracy. Additionally, agents have the flexibility to choose from various local training solvers, such as (stochastic) gradient descent and accelerated gradient descent. Further, we investigate how employing local training can enhance privacy, addressing point (ii). In particular, we derive differential privacy bounds and highlight their dependence on the number of local training epochs. We assess the effectiveness of the proposed algorithm by comparing it to alternative techniques, considering both theoretical analysis and numerical results from a classification task.", "authors": ["Nicola Bastianello", "Changxin Liu", "Karl H. Johansson"], "categories": ["cs.LG", "math.OC"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-03-26", "url": "https://arxiv.org/abs/2403.17572", "pdf_url": "https://arxiv.org/pdf/2403.17572v2", "arxiv_id": "2403.17572", "doi": "10.48550/arXiv.2403.17572", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "84164b1df5e29da04e275841fbc17f7ff190099c80507d5ed3da23c2ffdcaa9a", "sources": ["arxiv", "semantic_scholar"], "title": "The Privacy Policy Permission Model: A Unified View of Privacy Policies", "abstract": "Organizations use privacy policies to communicate their data collection practices to their clients. A privacy policy is a set of statements that specifies how an organization gathers, uses, discloses, and maintains a client's data. However, most privacy policies lack a clear, complete explanation of how data providers' information is used. We propose a modeling methodology, called the Privacy Policy Permission Model (PPPM), that provides a uniform, easy-to-understand representation of privacy policies, which can accurately and clearly show how data is used within an organization's practice. Using this methodology, a privacy policy is captured as a diagram. The diagram is capable of highlighting inconsistencies and inaccuracies in the privacy policy. The methodology supports privacy officers in properly and clearly articulating an organization's privacy policy.", "authors": ["Maryam Majedi", "Ken Barker"], "categories": ["cs.CR", "cs.CY"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-26", "url": "https://arxiv.org/abs/2403.17414", "pdf_url": "https://arxiv.org/pdf/2403.17414v1", "arxiv_id": "2403.17414", "doi": "10.48550/arXiv.2403.17414", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Transactions on Data Privacy", "quality_score": 0.1505}
{"id": "6a888921ebf39cc060735d5c9d7e6b2f4631e2e702cb6465347e0a4f39acb9ac", "sources": ["arxiv", "semantic_scholar"], "title": "Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation", "abstract": "In this article, we address the problem of federated learning in the presence of stragglers. For this problem, a coded federated learning framework has been proposed, where the central server aggregates gradients received from the non-stragglers and gradient computed from a privacy-preservation global coded dataset to mitigate the negative impact of the stragglers. However, when aggregating these gradients, fixed weights are consistently applied across iterations, neglecting the generation process of the global coded dataset and the dynamic nature of the trained model over iterations. This oversight may result in diminished learning performance. To overcome this drawback, we propose a new method named adaptive coded federated learning (ACFL). In ACFL, before the training, each device uploads a coded local dataset with additive noise to the central server to generate a global coded dataset under privacy preservation requirements. During each iteration of the training, the central server aggregates the gradients received from the non-stragglers and the gradient computed from the global coded dataset, where an adaptive policy for varying the aggregation weights is designed. Under this policy, we optimize the performance in terms of privacy and learning, where the learning performance is analyzed through convergence analysis and the privacy performance is characterized via mutual information differential privacy. Finally, we perform simulations to demonstrate the superiority of ACFL compared with the non-adaptive methods.", "authors": ["Chengxi Li", "Ming Xiao", "Mikael Skoglund"], "categories": ["eess.SP", "cs.CR", "cs.LG"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-03-22", "url": "https://arxiv.org/abs/2403.14905", "pdf_url": "https://arxiv.org/pdf/2403.14905v2", "arxiv_id": "2403.14905", "doi": "10.1109/TCOMM.2025.3594773", "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Communications", "quality_score": 0.2698}
{"id": "146239c06b15b57a62a2a1da36233ec01ea3f7bcc35222cca79a23138684b34d", "sources": ["arxiv", "semantic_scholar"], "title": "Improving LoRA in Privacy-preserving Federated Learning", "abstract": "Low-rank adaptation (LoRA) is one of the most popular task-specific parameter-efficient fine-tuning (PEFT) methods on pre-trained language models for its good performance and computational efficiency. LoRA injects a product of two trainable rank decomposition matrices over the top of each frozen pre-trained model module. However, when applied in the setting of privacy-preserving federated learning (FL), LoRA may become unstable due to the following facts: 1) the effects of data heterogeneity and multi-step local updates are non-negligible, 2) additive noise enforced on updating gradients to guarantee differential privacy (DP) can be amplified and 3) the final performance is susceptible to hyper-parameters. A key factor leading to these phenomena is the discordance between jointly optimizing the two low-rank matrices by local clients and separately aggregating them by the central server. Thus, this paper proposes an efficient and effective version of LoRA, Federated Freeze A LoRA (FFA-LoRA), to alleviate these challenges and further halve the communication cost of federated fine-tuning LLMs. The core idea of FFA-LoRA is to fix the randomly initialized non-zero matrices and only fine-tune the zero-initialized matrices. Compared to LoRA, FFA-LoRA is motivated by practical and theoretical benefits in privacy-preserved FL. Our experiments demonstrate that FFA-LoRA provides more consistent performance with better computational efficiency over vanilla LoRA in various FL tasks.", "authors": ["Youbang Sun", "Zitao Li", "Yaliang Li", "Bolin Ding"], "categories": ["cs.LG", "cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-18", "url": "https://arxiv.org/abs/2403.12313", "pdf_url": "https://arxiv.org/pdf/2403.12313v1", "arxiv_id": "2403.12313", "doi": "10.48550/arXiv.2403.12313", "citation_count": 193, "influential_citation_count": 30, "has_code": false, "code_url": null, "venue": "International Conference on Learning Representations", "quality_score": 0.7457}
{"id": "c539d56dabee3b630e10f8bb9317d61b188d385f5e62808970a86597323e9d71", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Transfer Learning with Differential Privacy", "abstract": "Federated learning has emerged as a powerful framework for analysing distributed data, yet two challenges remain pivotal: heterogeneity across sites and privacy of local data. In this paper, we address both challenges within a federated transfer learning framework, aiming to enhance learning on a target data set by leveraging information from multiple heterogeneous source data sets while adhering to privacy constraints. We rigorously formulate the notion of federated differential privacy, which offers privacy guarantees for each data set without assuming a trusted central server. Under this privacy model, we study four statistical problems: univariate mean estimation, low-dimensional linear regression, high-dimensional linear regression, and M-estimation. By investigating the minimax rates and quantifying the cost of privacy, we show that federated differential privacy is an intermediate privacy model between the well-established local and central models of differential privacy. Our analyses account for data heterogeneity and privacy, highlighting the fundamental costs associated with each factor and the benefits of knowledge transfer in federated learning.", "authors": ["Mengchu Li", "Ye Tian", "Yang Feng", "Yi Yu"], "categories": ["cs.LG", "cs.CR", "math.ST", "stat.ME", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-03-17", "url": "https://arxiv.org/abs/2403.11343", "pdf_url": "https://arxiv.org/pdf/2403.11343v4", "arxiv_id": "2403.11343", "doi": "10.48550/arXiv.2403.11343", "citation_count": 10, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2603}
{"id": "4156af5f8e4a74467f99a8ad7289c84af989ea1dfa4fa1bc8df335dceff4770f", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning Method for Preserving Privacy in Face Recognition System", "abstract": "The state-of-the-art face recognition systems are typically trained on a single computer, utilizing extensive image datasets collected from various number of users. However, these datasets often contain sensitive personal information that users may hesitate to disclose. To address potential privacy concerns, we explore the application of federated learning, both with and without secure aggregators, in the context of both supervised and unsupervised face recognition systems. Federated learning facilitates the training of a shared model without necessitating the sharing of individual private data, achieving this by training models on decentralized edge devices housing the data. In our proposed system, each edge device independently trains its own model, which is subsequently transmitted either to a secure aggregator or directly to the central server. To introduce diverse data without the need for data transmission, we employ generative adversarial networks to generate imposter data at the edge. Following this, the secure aggregator or central server combines these individual models to construct a global model, which is then relayed back to the edge devices. Experimental findings based on the CelebA datasets reveal that employing federated learning in both supervised and unsupervised face recognition systems offers dual benefits. Firstly, it safeguards privacy since the original data remains on the edge devices. Secondly, the experimental results demonstrate that the aggregated model yields nearly identical performance compared to the individual models, particularly when the federated model does not utilize a secure aggregator. Hence, our results shed light on the practical challenges associated with privacy-preserving face image training, particularly in terms of the balance between privacy and accuracy.", "authors": ["Enoch Solomon", "Abraham Woubie"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-08", "url": "https://arxiv.org/abs/2403.05344", "pdf_url": "https://arxiv.org/pdf/2403.05344v1", "arxiv_id": "2403.05344", "doi": "10.48550/arXiv.2403.05344", "citation_count": 12, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2785}
{"id": "38586accf225602256ef76e8bac601f05814f655007136f45774d07bb9e66f88", "sources": ["arxiv", "semantic_scholar"], "title": "Decoupled Subgraph Federated Learning", "abstract": "We address the challenge of federated learning on graph-structured data distributed across multiple clients. Specifically, we focus on the prevalent scenario of interconnected subgraphs, where interconnections between different clients play a critical role. We present a novel framework for this scenario, named FedStruct, that harnesses deep structural dependencies. To uphold privacy, unlike existing methods, FedStruct eliminates the necessity of sharing or generating sensitive node features or embeddings among clients. Instead, it leverages explicit global graph structure information to capture inter-node dependencies. We validate the effectiveness of FedStruct through experimental results conducted on six datasets for semi-supervised node classification, showcasing performance close to the centralized approach across various scenarios, including different data partitioning methods, varying levels of label availability, and number of clients.", "authors": ["Javad Aliakbari", "Johan Östman", "Alexandre Graell i Amat"], "categories": ["cs.LG", "cs.IT"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-02-29", "url": "https://arxiv.org/abs/2402.19163", "pdf_url": "https://arxiv.org/pdf/2402.19163v3", "arxiv_id": "2402.19163", "doi": null, "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Learning Representations", "quality_score": 0.1505}
{"id": "6d636eaf57e98734768e8e6a9f32b5096321e9ec0ca1a1fb56fcc140697afbe7", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Distributed Optimization and Learning", "abstract": "Distributed optimization and learning has recently garnered great attention due to its wide applications in sensor networks, smart grids, machine learning, and so forth. Despite rapid development, existing distributed optimization and learning algorithms require each agent to exchange messages with its neighbors, which may expose sensitive information and raise significant privacy concerns. In this survey paper, we overview privacy-preserving distributed optimization and learning methods. We first discuss cryptography, differential privacy, and other techniques that can be used for privacy preservation and indicate their pros and cons for privacy protection in distributed optimization and learning. We believe that among these approaches, differential privacy is most promising due to its low computational and communication complexities, which are extremely appealing for modern learning based applications with high dimensions of optimization variables. We then introduce several differential-privacy algorithms that can simultaneously ensure privacy and optimization accuracy. Moreover, we provide example applications in several machine learning problems to confirm the real-world effectiveness of these algorithms. Finally, we highlight some challenges in this research domain and discuss future directions.", "authors": ["Ziqin Chen", "Yongqiang Wang"], "categories": ["cs.LG", "cs.CR", "cs.GT"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-29", "url": "https://arxiv.org/abs/2403.00157", "pdf_url": "https://arxiv.org/pdf/2403.00157v1", "arxiv_id": "2403.00157", "doi": "10.48550/arXiv.2403.00157", "citation_count": 7, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2258}
{"id": "f854d711d02bc7c6dcc4c519e62513d39b9094f7bb0c873300b27273a2a0d23d", "sources": ["arxiv", "semantic_scholar"], "title": "A privacy-preserving, distributed and cooperative FCM-based learning approach for cancer research", "abstract": "Distributed Artificial Intelligence is attracting interest day by day. In this paper, the authors introduce an innovative methodology for distributed learning of Particle Swarm Optimization-based Fuzzy Cognitive Maps in a privacy-preserving way. The authors design a training scheme for collaborative FCM learning that offers data privacy compliant with the current regulation. This method is applied to a cancer detection problem, proving that the performance of the model is improved by the Federated Learning process, and obtaining similar results to the ones that can be found in the literature.", "authors": ["Jose L. Salmeron", "Irina Arévalo"], "categories": ["cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-15", "url": "https://arxiv.org/abs/2402.10102", "pdf_url": "https://arxiv.org/pdf/2402.10102v2", "arxiv_id": "2402.10102", "doi": "10.1007/978-3-030-52705-1_35", "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2698}
{"id": "a5abd3a9d385cd33f0ba7447575f418e0076ee38c539906b71974ba8d3ae6c36", "sources": ["arxiv", "semantic_scholar"], "title": "Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning", "abstract": "In the evolving field of machine learning, ensuring group fairness has become a critical concern, prompting the development of algorithms designed to mitigate bias in decision-making processes. Group fairness refers to the principle that a model's decisions should be equitable across different groups defined by sensitive attributes such as gender or race, ensuring that individuals from privileged groups and unprivileged groups are treated fairly and receive similar outcomes. However, achieving fairness in the presence of group-specific concept drift remains an unexplored frontier, and our research represents pioneering efforts in this regard. Group-specific concept drift refers to situations where one group experiences concept drift over time while another does not, leading to a decrease in fairness even if accuracy remains fairly stable. Within the framework of Federated Learning, where clients collaboratively train models, its distributed nature further amplifies these challenges since each client can experience group-specific concept drift independently while still sharing the same underlying concept, creating a complex and dynamic environment for maintaining fairness. The most significant contribution of our research is the formalization and introduction of the problem of group-specific concept drift and its distributed counterpart, shedding light on its critical importance in the field of fairness. Additionally, leveraging insights from prior research, we adapt an existing distributed concept drift adaptation algorithm to tackle group-specific distributed concept drift which uses a multi-model approach, a local group-specific drift detection mechanism, and continuous clustering of models over time. The findings from our experiments highlight the importance of addressing group-specific concept drift and its distributed counterpart to advance fairness in machine learning.", "authors": ["Teresa Salazar", "João Gama", "Helder Araújo", "Pedro Henriques Abreu"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2024-02-12", "url": "https://arxiv.org/abs/2402.07586", "pdf_url": "https://arxiv.org/pdf/2402.07586v4", "arxiv_id": "2402.07586", "doi": "10.1109/TNNLS.2025.3601834", "citation_count": 10, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Neural Networks and Learning Systems", "quality_score": 0.2603}
{"id": "a9cb3f821694f6f8d7dd155a74fbb3b2e97a6e4aac6ae54feb387b35897c184c", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning with Differential Privacy", "abstract": "Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving client's private data from being shared among different parties. Nevertheless, private information can still be divulged by analyzing uploaded parameter weights from clients. In this report, we showcase our empirical benchmark of the effect of the number of clients and the addition of differential privacy (DP) mechanisms on the performance of the model on different types of data. Our results show that non-i.i.d and small datasets have the highest decrease in performance in a distributed and differentially private setting.", "authors": ["Adrien Banse", "Jan Kreischer", "Xavier Oliva i Jürgens"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-03", "url": "https://arxiv.org/abs/2402.02230", "pdf_url": "https://arxiv.org/pdf/2402.02230v1", "arxiv_id": "2402.02230", "doi": "10.48550/arXiv.2402.02230", "citation_count": 13, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2865}
{"id": "055b569b679ac07bf73da294aa8de64996279fa645bbe5a7a9eeda5bbb50170e", "sources": ["arxiv", "semantic_scholar"], "title": "Survey of Privacy Threats and Countermeasures in Federated Learning", "abstract": "Federated learning is widely considered to be as a privacy-aware learning method because no training data is exchanged directly between clients. Nevertheless, there are threats to privacy in federated learning, and privacy countermeasures have been studied. However, we note that common and unique privacy threats among typical types of federated learning have not been categorized and described in a comprehensive and specific way. In this paper, we describe privacy threats and countermeasures for the typical types of federated learning; horizontal federated learning, vertical federated learning, and transfer federated learning.", "authors": ["Masahiro Hayashitani", "Junki Mori", "Isamu Teranishi"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-01", "url": "https://arxiv.org/abs/2402.00342", "pdf_url": "https://arxiv.org/pdf/2402.00342v2", "arxiv_id": "2402.00342", "doi": "10.1109/FLTA67013.2025.11336767", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1193}
{"id": "0e1504a61a7d4c12ef6b8ab967e38f0f61247824bb13096df97e409ad3649efb", "sources": ["arxiv", "semantic_scholar"], "title": "Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network", "abstract": "Edge caching is a promising solution for next-generation networks by empowering caching units in small-cell base stations (SBSs), which allows user equipments (UEs) to fetch users' requested contents that have been pre-cached in SBSs. It is crucial for SBSs to predict accurate popular contents through learning while protecting users' personal information. Traditional federated learning (FL) can protect users' privacy but the data discrepancies among UEs can lead to a degradation in model quality. Therefore, it is necessary to train personalized local models for each UE to predict popular contents accurately. In addition, the cached contents can be shared among adjacent SBSs in next-generation networks, thus caching predicted popular contents in different SBSs may affect the cost to fetch contents. Hence, it is critical to determine where the popular contents are cached cooperatively. To address these issues, we propose a cooperative edge caching scheme based on elastic federated and multi-agent deep reinforcement learning (CEFMR) to optimize the cost in the network. We first propose an elastic FL algorithm to train the personalized model for each UE, where adversarial autoencoder (AAE) model is adopted for training to improve the prediction accuracy, then {a popular} content prediction algorithm is proposed to predict the popular contents for each SBS based on the trained AAE model. Finally, we propose a multi-agent deep reinforcement learning (MADRL) based algorithm to decide where the predicted popular contents are collaboratively cached among SBSs. Our experimental results demonstrate the superiority of our proposed scheme to existing baseline caching schemes.", "authors": ["Qiong Wu", "Wenhua Wang", "Pingyi Fan", "Qiang Fan", "Huiling Zhu", "Khaled B. Letaief"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-01-18", "url": "https://arxiv.org/abs/2401.09886", "pdf_url": "https://arxiv.org/pdf/2401.09886v2", "arxiv_id": "2401.09886", "doi": "10.1109/TNSM.2024.3403842", "citation_count": 65, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/qiongwu86/Edge-Caching-Based-on-Multi-Agent-Deep-Reinforcement-Learning-and-Federated-Learning", "venue": "IEEE Transactions on Network and Service Management", "quality_score": 0.4549}
{"id": "1af0802819851e601316f9a40a52d00b30503c155262c87412d36705d5c68970", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving in Blockchain-based Federated Learning Systems", "abstract": "Federated Learning (FL) has recently arisen as a revolutionary approach to collaborative training Machine Learning models. According to this novel framework, multiple participants train a global model collaboratively, coordinating with a central aggregator without sharing their local data. As FL gains popularity in diverse domains, security, and privacy concerns arise due to the distributed nature of this solution. Therefore, integrating this strategy with Blockchain technology has been consolidated as a preferred choice to ensure the privacy and security of participants. This paper explores the research efforts carried out by the scientific community to define privacy solutions in scenarios adopting Blockchain-Enabled FL. It comprehensively summarizes the background related to FL and Blockchain, evaluates existing architectures for their integration, and the primary attacks and possible countermeasures to guarantee privacy in this setting. Finally, it reviews the main application scenarios where Blockchain-Enabled FL approaches have been proficiently applied. This survey can help academia and industry practitioners understand which theories and techniques exist to improve the performance of FL through Blockchain to preserve privacy and which are the main challenges and future directions in this novel and still under-explored context. We believe this work provides a novel contribution respect to the previous surveys and is a valuable tool to explore the current landscape, understand perspectives, and pave the way for advancements or improvements in this amalgamation of Blockchain and Federated Learning.", "authors": ["Sameera K. M.", "Serena Nicolazzo", "Marco Arazzi", "Antonino Nocera", "Rafidha Rehiman K. A.", "Vinod P", "Mauro Conti"], "categories": ["cs.CR", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-01-07", "url": "https://arxiv.org/abs/2401.03552", "pdf_url": "https://arxiv.org/pdf/2401.03552v1", "arxiv_id": "2401.03552", "doi": "10.1016/j.comcom.2024.04.024", "citation_count": 81, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Computer Communications", "quality_score": 0.4785}
{"id": "7ba75a6a9a4e207c0f57357a842e0bbd7677bd8ac727d255f03330199074c6f7", "sources": ["arxiv", "semantic_scholar"], "title": "Federated learning-outcome prediction with multi-layer privacy protection", "abstract": "Learning-outcome prediction (LOP) is a long-standing and critical problem in educational routes. Many studies have contributed to developing effective models while often suffering from data shortage and low generalization to various institutions due to the privacy-protection issue. To this end, this study proposes a distributed grade prediction model, dubbed FecMap, by exploiting the federated learning (FL) framework that preserves the private data of local clients and communicates with others through a global generalized model. FecMap considers local subspace learning (LSL), which explicitly learns the local features against the global features, and multi-layer privacy protection (MPP), which hierarchically protects the private features, including model-shareable features and not-allowably shared features, to achieve client-specific classifiers of high performance on LOP per institution. FecMap is then achieved in an iteration manner with all datasets distributed on clients by training a local neural network composed of a global part, a local part, and a classification head in clients and averaging the global parts from clients on the server. To evaluate the FecMap model, we collected three higher-educational datasets of student academic records from engineering majors. Experiment results manifest that FecMap benefits from the proposed LSL and MPP and achieves steady performance on the task of LOP, compared with the state-of-the-art models. This study makes a fresh attempt at the use of federated learning in the learning-analytical task, potentially paving the way to facilitating personalized education with privacy protection.", "authors": ["Yupei Zhang", "Yuxin Li", "Yifei Wang", "Shuangshuang Wei", "Yunan Xu", "Xuequn Shang"], "categories": ["cs.LG", "cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2023-12-25", "url": "https://arxiv.org/abs/2312.15608", "pdf_url": "https://arxiv.org/pdf/2312.15608v1", "arxiv_id": "2312.15608", "doi": "10.1007/s11704-023-2791-8", "citation_count": 16, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Frontiers of Computer Science, 2024,18(6):186604", "quality_score": 0.3076}
{"id": "20098da7629044d3d718bf363b880a0745f498b34e4e703fd3ae5faf6b76fbba", "sources": ["arxiv", "semantic_scholar"], "title": "An Empirical Study of Efficiency and Privacy of Federated Learning Algorithms", "abstract": "In today's world, the rapid expansion of IoT networks and the proliferation of smart devices in our daily lives, have resulted in the generation of substantial amounts of heterogeneous data. These data forms a stream which requires special handling. To handle this data effectively, advanced data processing technologies are necessary to guarantee the preservation of both privacy and efficiency. Federated learning emerged as a distributed learning method that trains models locally and aggregates them on a server to preserve data privacy. This paper showcases two illustrative scenarios that highlight the potential of federated learning (FL) as a key to delivering efficient and privacy-preserving machine learning within IoT networks. We first give the mathematical foundations for key aggregation algorithms in federated learning, i.e., FedAvg and FedProx. Then, we conduct simulations, using Flower Framework, to show the \\textit{efficiency} of these algorithms by training deep neural networks on common datasets and show a comparison between the accuracy and loss metrics of FedAvg and FedProx. Then, we present the results highlighting the trade-off between maintaining privacy versus accuracy via simulations - involving the implementation of the differential privacy (DP) method - in Pytorch and Opacus ML frameworks on common FL datasets and data distributions for both FedAvg and FedProx strategies.", "authors": ["Sofia Zahri", "Hajar Bennouri", "Ahmed M. Abdelmoniem"], "categories": ["cs.LG", "cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2023-12-24", "url": "https://arxiv.org/abs/2312.15375", "pdf_url": "https://arxiv.org/pdf/2312.15375v1", "arxiv_id": "2312.15375", "doi": "10.48550/arXiv.2312.15375", "citation_count": 6, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "16fabfeea49ad840edd2b4bba85aee2449d83f8842ddc111a260e95662346dc3", "sources": ["arxiv", "semantic_scholar"], "title": "Federated learning with differential privacy and an untrusted aggregator", "abstract": "Federated learning for training models over mobile devices is gaining popularity. Current systems for this task exhibit significant trade-offs between model accuracy, privacy guarantee, and device efficiency. For instance, Oort (OSDI 2021) provides excellent accuracy and efficiency but requires a trusted central server. On the other hand, Orchard (OSDI 2020) provides good accuracy and the rigorous guarantee of differential privacy over an untrusted server, but creates huge overhead for the devices. This paper describes Aero, a new federated learning system that significantly improves this trade-off. Aero guarantees good accuracy, differential privacy over an untrusted server, and keeps the device overhead low. The key idea of Aero is to tune system architecture and design to a specific set of popular, federated learning algorithms. This tuning requires novel optimizations and techniques, e.g., a new protocol to securely aggregate updates from devices. An evaluation of Aero demonstrates that it provides comparable accuracy to plain federated learning (without differential privacy), and it improves efficiency (CPU and network) over Orchard by up to $10^5\\times$.", "authors": ["Kunlong Liu", "Trinabh Gupta"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2023-12-17", "url": "https://arxiv.org/abs/2312.10789", "pdf_url": "https://arxiv.org/pdf/2312.10789v2", "arxiv_id": "2312.10789", "doi": "10.5220/0012322100003648", "citation_count": 5, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "International Conference on Information Systems Security and Privacy", "quality_score": 0.1945}
{"id": "c700c3c50c86df502e14a435af64cf1584bc8d79acfa3af837f687a50bc2a982", "sources": ["arxiv", "semantic_scholar"], "title": "PPIDSG: A Privacy-Preserving Image Distribution Sharing Scheme with GAN in Federated Learning", "abstract": "Federated learning (FL) has attracted growing attention since it allows for privacy-preserving collaborative training on decentralized clients without explicitly uploading sensitive data to the central server. However, recent works have revealed that it still has the risk of exposing private data to adversaries. In this paper, we conduct reconstruction attacks and enhance inference attacks on various datasets to better understand that sharing trained classification model parameters to a central server is the main problem of privacy leakage in FL. To tackle this problem, a privacy-preserving image distribution sharing scheme with GAN (PPIDSG) is proposed, which consists of a block scrambling-based encryption algorithm, an image distribution sharing method, and local classification training. Specifically, our method can capture the distribution of a target image domain which is transformed by the block encryption algorithm, and upload generator parameters to avoid classifier sharing with negligible influence on model performance. Furthermore, we apply a feature extractor to motivate model utility and train it separately from the classifier. The extensive experimental results and security analyses demonstrate the superiority of our proposed scheme compared to other state-of-the-art defense methods. The code is available at https://github.com/ytingma/PPIDSG.", "authors": ["Yuting Ma", "Yuanzhi Yao", "Xiaohua Xu"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-12-16", "url": "https://arxiv.org/abs/2312.10380", "pdf_url": "https://arxiv.org/pdf/2312.10380v1", "arxiv_id": "2312.10380", "doi": "10.48550/arXiv.2312.10380", "citation_count": 9, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/ytingma/PPIDSG", "venue": "AAAI Conference on Artificial Intelligence", "quality_score": 0.25}
{"id": "9ba7e2d14fe7c42875e6f67a6e150da8dcb17796f3c60a901d4f568079c69176", "sources": ["arxiv", "semantic_scholar"], "title": "A Distributed Privacy Preserving Model for the Detection of Alzheimer's Disease", "abstract": "In the era of rapidly advancing medical technologies, the segmentation of medical data has become inevitable, necessitating the development of privacy preserving machine learning algorithms that can train on distributed data. Consolidating sensitive medical data is not always an option particularly due to the stringent privacy regulations imposed by the Health Insurance Portability and Accountability Act (HIPAA). In this paper, I introduce a HIPAA compliant framework that can train from distributed data. I then propose a multimodal vertical federated model for Alzheimer's Disease (AD) detection, a serious neurodegenerative condition that can cause dementia, severely impairing brain function and hindering simple tasks, especially without preventative care. This vertical federated learning (VFL) model offers a distributed architecture that enables collaborative learning across diverse sources of medical data while respecting privacy constraints imposed by HIPAA. The VFL architecture proposed herein offers a novel distributed architecture, enabling collaborative learning across diverse sources of medical data while respecting statutory privacy constraints. By leveraging multiple modalities of data, the robustness and accuracy of AD detection can be enhanced. This model not only contributes to the advancement of federated learning techniques but also holds promise for overcoming the hurdles posed by data segmentation in medical research.", "authors": ["Paul K. Mandal"], "categories": ["cs.LG", "cs.AI", "cs.CV", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2023-12-15", "url": "https://arxiv.org/abs/2312.10237", "pdf_url": "https://arxiv.org/pdf/2312.10237v5", "arxiv_id": "2312.10237", "doi": "10.1007/s00521-024-10419-4", "citation_count": 8, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Neural Comput & Applic (2024)", "quality_score": 0.2386}
{"id": "f013143a69ff701be375438761a4aed25da0adc5151673a8a327846bcc011a40", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-preserving quantum federated learning via gradient hiding", "abstract": "Distributed quantum computing, particularly distributed quantum machine learning, has gained substantial prominence for its capacity to harness the collective power of distributed quantum resources, transcending the limitations of individual quantum nodes. Meanwhile, the critical concern of privacy within distributed computing protocols remains a significant challenge, particularly in standard classical federated learning (FL) scenarios where data of participating clients is susceptible to leakage via gradient inversion attacks by the server. This paper presents innovative quantum protocols with quantum communication designed to address the FL problem, strengthen privacy measures, and optimize communication efficiency. In contrast to previous works that leverage expressive variational quantum circuits or differential privacy techniques, we consider gradient information concealment using quantum states and propose two distinct FL protocols, one based on private inner-product estimation and the other on incremental learning. These protocols offer substantial advancements in privacy preservation with low communication resources, forging a path toward efficient quantum communication-assisted FL protocols and contributing to the development of secure distributed quantum machine learning, thus addressing critical privacy concerns in the quantum computing era.", "authors": ["Changhao Li", "Niraj Kumar", "Zhixin Song", "Shouvanik Chakrabarti", "Marco Pistoia"], "categories": ["quant-ph", "cs.CR", "cs.DC", "cs.LG"], "fields_of_study": ["Physics", "Computer Science"], "published_date": "2023-12-07", "url": "https://arxiv.org/abs/2312.04447", "pdf_url": "https://arxiv.org/pdf/2312.04447v1", "arxiv_id": "2312.04447", "doi": "10.1088/2058-9565/ad40cc", "citation_count": 37, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Quantum Science and Technology", "quality_score": 0.3949}
{"id": "4ec3ed74c31415532f6781aac5e56a9af330933105f3041fdf87906440aafd76", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning is Better with Non-Homomorphic Encryption", "abstract": "Traditional AI methodologies necessitate centralized data collection, which becomes impractical when facing problems with network communication, data privacy, or storage capacity. Federated Learning (FL) offers a paradigm that empowers distributed AI model training without collecting raw data. There are different choices for providing privacy during FL training. One of the popular methodologies is employing Homomorphic Encryption (HE) - a breakthrough in privacy-preserving computation from Cryptography. However, these methods have a price in the form of extra computation and memory footprint. To resolve these issues, we propose an innovative framework that synergizes permutation-based compressors with Classical Cryptography, even though employing Classical Cryptography was assumed to be impossible in the past in the context of FL. Our framework offers a way to replace HE with cheaper Classical Cryptography primitives which provides security for the training process. It fosters asynchronous communication and provides flexible deployment options in various communication topologies.", "authors": ["Konstantin Burlachenko", "Abdulmajeed Alrowithi", "Fahad Ali Albalawi", "Peter Richtarik"], "categories": ["cs.CR", "cs.LG", "math.OC"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2023-12-04", "url": "https://arxiv.org/abs/2312.02074", "pdf_url": "https://arxiv.org/pdf/2312.02074v1", "arxiv_id": "2312.02074", "doi": "10.1145/3630048.3630182", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Proceedings of the 4th International Workshop on Distributed Machine Learning December 2023", "quality_score": 0.2113}
{"id": "5890630a18334257c25a09ac9f00bfb6bcb341326132338d512e672c435718c8", "sources": ["arxiv", "semantic_scholar"], "title": "Evaluating Multi-Global Server Architecture for Federated Learning", "abstract": "Federated learning (FL) with a single global server framework is currently a popular approach for training machine learning models on decentralized environment, such as mobile devices and edge devices. However, the centralized server architecture poses a risk as any challenge on the central/global server would result in the failure of the entire system. To minimize this risk, we propose a novel federated learning framework that leverages the deployment of multiple global servers. We posit that implementing multiple global servers in federated learning can enhance efficiency by capitalizing on local collaborations and aggregating knowledge, and the error tolerance in regard to communication failure in the single server framework would be handled. We therefore propose a novel framework that leverages the deployment of multiple global servers. We conducted a series of experiments using a dataset containing the event history of electric vehicle (EV) charging at numerous stations. We deployed a federated learning setup with multiple global servers and client servers, where each client-server strategically represented a different region and a global server was responsible for aggregating local updates from those devices. Our preliminary results of the global models demonstrate that the difference in performance attributed to multiple servers is less than 1%. While the hypothesis of enhanced model efficiency was not as expected, the rule for handling communication challenges added to the algorithm could resolve the error tolerance issue. Future research can focus on identifying specific uses for the deployment of multiple global servers.", "authors": ["Asfia Kawnine", "Hung Cao", "Atah Nuh Mih", "Monica Wachowicz"], "categories": ["cs.LG", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2023-11-26", "url": "https://arxiv.org/abs/2311.15382", "pdf_url": "https://arxiv.org/pdf/2311.15382v1", "arxiv_id": "2311.15382", "doi": "10.1109/ICCE59016.2024.10444349", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Consumer Electronics", "quality_score": 0.1193}
{"id": "6a2ea470b5025738d4e5fa71dedfa29cd8ac3cd88ac57b12ce091aad5a7c815b", "sources": ["arxiv", "semantic_scholar"], "title": "Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks", "abstract": "5G and Beyond Networks become increasingly complex and heterogeneous, with diversified and high requirements from a wide variety of emerging applications. The complexity and diversity of Telecom networks place an increasing strain on maintenance and operation efforts. Moreover, the strict security and privacy requirements present a challenge for mobile operators to leverage network data. To detect network faults, and mitigate future failures, prior work focused on leveraging traditional ML/DL methods to locate anomalies in networks. The current approaches, although powerful, do not consider the intertwined nature of embedded and software-intensive Radio Access Network systems. In this paper, we propose a Bi-level Federated Graph Neural Network anomaly detection and diagnosis model that is able to detect anomalies in Telecom networks in a privacy-preserving manner, while minimizing communication costs. Our method revolves around conceptualizing Telecom data as a bi-level temporal Graph Neural Networks. The first graph captures the interactions between different RAN nodes that are exposed to different deployment scenarios in the network, while each individual Radio Access Network node is further elaborated into its software (SW) execution graph. Additionally, we use Federated Learning to address privacy and security limitations. Furthermore, we study the performance of anomaly detection model under three settings: (1) Centralized (2) Federated Learning and (3) Personalized Federated Learning using real-world data from an operational network. Our comprehensive experiments showed that Personalized Federated Temporal Graph Neural Networks method outperforms the most commonly used techniques for Anomaly Detection.", "authors": ["R. Bourgerie", "T. Zanouda"], "categories": ["cs.LG", "cs.NI"], "fields_of_study": ["Computer Science"], "published_date": "2023-11-24", "url": "https://arxiv.org/abs/2311.14469", "pdf_url": "https://arxiv.org/pdf/2311.14469v1", "arxiv_id": "2311.14469", "doi": "10.1109/ICDMW60847.2023.10449399", "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2698}
{"id": "91350df94f4adb41230fd340fe57ea85cdd92ab978356cbf143f59953f1e3c5c", "sources": ["arxiv", "semantic_scholar"], "title": "FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning", "abstract": "Federated Learning (FL) is a collaborative method for training models while preserving data privacy in decentralized settings. However, FL encounters challenges related to data heterogeneity, which can result in performance degradation. In our study, we observe that as data heterogeneity increases, feature representation in the FedAVG model deteriorates more significantly compared to classifier weight. Additionally, we observe that as data heterogeneity increases, the gap between higher feature norms for observed classes, obtained from local models, and feature norms of unobserved classes widens, in contrast to the behavior of classifier weight norms. This widening gap extends to encompass the feature norm disparities between local and the global models. To address these issues, we introduce Federated Averaging with Feature Normalization Update (FedFN), a straightforward learning method. We demonstrate the superior performance of FedFN through extensive experiments, even when applied to pretrained ResNet18. Subsequently, we confirm the applicability of FedFN to foundation models.", "authors": ["Seongyoon Kim", "Gihun Lee", "Jaehoon Oh", "Se-Young Yun"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2023-11-22", "url": "https://arxiv.org/abs/2311.13267", "pdf_url": "https://arxiv.org/pdf/2311.13267v1", "arxiv_id": "2311.13267", "doi": "10.48550/arXiv.2311.13267", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.25}
{"id": "bea9e0639827bd170a39d559e3dfebfd7fbc6a4f897fdd2b127e1576ecfe43d3", "sources": ["arxiv", "semantic_scholar"], "title": "FedCPC: An Effective Federated Contrastive Learning Method for Privacy Preserving Early-Stage Alzheimer's Speech Detection", "abstract": "The early-stage Alzheimer's disease (AD) detection has been considered an important field of medical studies. Like traditional machine learning methods, speech-based automatic detection also suffers from data privacy risks because the data of specific patients are exclusive to each medical institution. A common practice is to use federated learning to protect the patients' data privacy. However, its distributed learning process also causes performance reduction. To alleviate this problem while protecting user privacy, we propose a federated contrastive pre-training (FedCPC) performed before federated training for AD speech detection, which can learn a better representation from raw data and enables different clients to share data in the pre-training and training stages. Experimental results demonstrate that the proposed methods can achieve satisfactory performance while preserving data privacy.", "authors": ["Wenqing Wei", "Zhengdong Yang", "Yuan Gao", "Jiyi Li", "Chenhui Chu", "Shogo Okada", "Sheng Li"], "categories": ["eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2023-11-21", "url": "https://arxiv.org/abs/2311.13043", "pdf_url": "https://arxiv.org/pdf/2311.13043v1", "arxiv_id": "2311.13043", "doi": "10.1109/ASRU57964.2023.10389690", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Automatic Speech Recognition & Understanding", "quality_score": 0.1505}
{"id": "a66a777cc3f4b3fd7f8654e37584260fd833ff34b3d4a5f322695e157b6b40e5", "sources": ["arxiv", "semantic_scholar"], "title": "Contribution Evaluation in Federated Learning: Examining Current Approaches", "abstract": "Federated Learning (FL) has seen increasing interest in cases where entities want to collaboratively train models while maintaining privacy and governance over their data. In FL, clients with private and potentially heterogeneous data and compute resources come together to train a common model without raw data ever leaving their locale. Instead, the participants contribute by sharing local model updates, which, naturally, differ in quality. Quantitatively evaluating the worth of these contributions is termed the Contribution Evaluation (CE) problem. We review current CE approaches from the underlying mathematical framework to efficiently calculate a fair value for each client. Furthermore, we benchmark some of the most promising state-of-the-art approaches, along with a new one we introduce, on MNIST and CIFAR-10, to showcase their differences. Designing a fair and efficient CE method, while a small part of the overall FL system design, is tantamount to the mainstream adoption of FL.", "authors": ["Vasilis Siomos", "Jonathan Passerat-Palmbach"], "categories": ["cs.LG", "cs.DC", "cs.GT"], "fields_of_study": ["Computer Science"], "published_date": "2023-11-16", "url": "https://arxiv.org/abs/2311.09856", "pdf_url": "https://arxiv.org/pdf/2311.09856v1", "arxiv_id": "2311.09856", "doi": "10.48550/arXiv.2311.09856", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2258}
{"id": "eff953ac2742cf2498c58a523153e9e9ccbe3b57ae5d54e8e3b1c6404953f5dc", "sources": ["arxiv", "semantic_scholar"], "title": "Communication Efficient and Privacy-Preserving Federated Learning Based on Evolution Strategies", "abstract": "Federated learning (FL) is an emerging paradigm for training deep neural networks (DNNs) in distributed manners. Current FL approaches all suffer from high communication overhead and information leakage. In this work, we present a federated learning algorithm based on evolution strategies (FedES), a zeroth-order training method. Instead of transmitting model parameters, FedES only communicates loss values, and thus has very low communication overhead. Moreover, a third party is unable to estimate gradients without knowing the pre-shared seed, which protects data privacy. Experimental results demonstrate FedES can achieve the above benefits while keeping convergence performance the same as that with back propagation methods.", "authors": ["Guangchen Lan"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-11-05", "url": "https://arxiv.org/abs/2311.03405", "pdf_url": "https://arxiv.org/pdf/2311.03405v2", "arxiv_id": "2311.03405", "doi": "10.48550/arXiv.2311.03405", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "7fb443be7e1605927b06fb4e864882728f9b21bf74b5d34d79865aeca05ebde8", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection", "abstract": "The effective detection of evidence of financial anomalies requires collaboration among multiple entities who own a diverse set of data, such as a payment network system (PNS) and its partner banks. Trust among these financial institutions is limited by regulation and competition. Federated learning (FL) enables entities to collaboratively train a model when data is either vertically or horizontally partitioned across the entities. However, in real-world financial anomaly detection scenarios, the data is partitioned both vertically and horizontally and hence it is not possible to use existing FL approaches in a plug-and-play manner. Our novel solution, PV4FAD, combines fully homomorphic encryption (HE), secure multi-party computation (SMPC), differential privacy (DP), and randomization techniques to balance privacy and accuracy during training and to prevent inference threats at model deployment time. Our solution provides input privacy through HE and SMPC, and output privacy against inference time attacks through DP. Specifically, we show that, in the honest-but-curious threat model, banks do not learn any sensitive features about PNS transactions, and the PNS does not learn any information about the banks' dataset but only learns prediction labels. We also develop and analyze a DP mechanism to protect output privacy during inference. Our solution generates high-utility models by significantly reducing the per-bank noise level while satisfying distributed DP. To ensure high accuracy, our approach produces an ensemble model, in particular, a random forest. This enables us to take advantage of the well-known properties of ensembles to reduce variance and increase accuracy. Our solution won second prize in the first phase of the U.S. Privacy Enhancing Technologies (PETs) Prize Challenge.", "authors": ["Swanand Ravindra Kadhe", "Heiko Ludwig", "Nathalie Baracaldo", "Alan King", "Yi Zhou", "Keith Houck", "Ambrish Rawat", "Mark Purcell", "Naoise Holohan", "Mikio Takeuchi", "Ryo Kawahara", "Nir Drucker", "Hayim Shaul", "Eyal Kushnir", "Omri Soceanu"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-10-30", "url": "https://arxiv.org/abs/2310.19304", "pdf_url": "https://arxiv.org/pdf/2310.19304v1", "arxiv_id": "2310.19304", "doi": "10.48550/arXiv.2310.19304", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "a666151abb96ac610fdfeda8d26b1b7a548ce4f42caf34a8053383d4368a4fe1", "sources": ["arxiv", "semantic_scholar"], "title": "Serverless Federated Learning with flwr-serverless", "abstract": "Federated learning is becoming increasingly relevant and popular as we witness a surge in data collection and storage of personally identifiable information. Alongside these developments there have been many proposals from governments around the world to provide more protections for individuals' data and a heightened interest in data privacy measures. As deep learning continues to become more relevant in new and existing domains, it is vital to develop strategies like federated learning that can effectively train data from different sources, such as edge devices, without compromising security and privacy. Recently, the Flower (\\texttt{Flwr}) Python package was introduced to provide a scalable, flexible, and easy-to-use framework for implementing federated learning. However, to date, Flower is only able to run synchronous federated learning which can be costly and time-consuming to run because the process is bottlenecked by client-side training jobs that are slow or fragile. Here, we introduce \\texttt{flwr-serverless}, a wrapper around the Flower package that extends its functionality to allow for both synchronous and asynchronous federated learning with minimal modification to Flower's design paradigm. Furthermore, our approach to federated learning allows the process to run without a central server, which increases the domains of application and accessibility of its use. This paper presents the design details and usage of this approach through a series of experiments that were conducted using public datasets. Overall, we believe that our approach decreases the time and cost to run federated training and provides an easier way to implement and experiment with federated learning systems.", "authors": ["Sanjeev V. Namjoshi", "Reese Green", "Krishi Sharma", "Zhangzhang Si"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-10-23", "url": "https://arxiv.org/abs/2310.15329", "pdf_url": "https://arxiv.org/pdf/2310.15329v1", "arxiv_id": "2310.15329", "doi": "10.48550/arXiv.2310.15329", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "736759510d64cdf972144e1f1185ebc05f1e97349c8cbef153d65d598f8c89a6", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Quantum Machine Learning with Differential Privacy", "abstract": "The preservation of privacy is a critical concern in the implementation of artificial intelligence on sensitive training data. There are several techniques to preserve data privacy but quantum computations are inherently more secure due to the no-cloning theorem, resulting in a most desirable computational platform on top of the potential quantum advantages. There have been prior works in protecting data privacy by Quantum Federated Learning (QFL) and Quantum Differential Privacy (QDP) studied independently. However, to the best of our knowledge, no prior work has addressed both QFL and QDP together yet. Here, we propose to combine these privacy-preserving methods and implement them on the quantum platform, so that we can achieve comprehensive protection against data leakage (QFL) and model inversion attacks (QDP). This implementation promises more efficient and secure artificial intelligence. In this paper, we present a successful implementation of these privacy-preservation methods by performing the binary classification of the Cats vs Dogs dataset. Using our quantum-classical machine learning model, we obtained a test accuracy of over 0.98, while maintaining epsilon values less than 1.3. We show that federated differentially private training is a viable privacy preservation method for quantum machine learning on Noisy Intermediate-Scale Quantum (NISQ) devices.", "authors": ["Rod Rofougaran", "Shinjae Yoo", "Huan-Hsin Tseng", "Samuel Yen-Chi Chen"], "categories": ["quant-ph", "cs.LG"], "fields_of_study": ["Physics", "Computer Science"], "published_date": "2023-10-10", "url": "https://arxiv.org/abs/2310.06973", "pdf_url": "https://arxiv.org/pdf/2310.06973v1", "arxiv_id": "2310.06973", "doi": "10.1109/ICASSP48485.2024.10447155", "citation_count": 45, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Acoustics, Speech, and Signal Processing", "quality_score": 0.4157}
{"id": "aa9f1424f8b4e9380f9d85db541a6902d3d6c503e1d865dd3942c9f39b91a1c5", "sources": ["arxiv", "semantic_scholar"], "title": "FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler", "abstract": "Cross-silo federated learning offers a promising solution to collaboratively train robust and generalized AI models without compromising the privacy of local datasets, e.g., healthcare, financial, as well as scientific projects that lack a centralized data facility. Nonetheless, because of the disparity of computing resources among different clients (i.e., device heterogeneity), synchronous federated learning algorithms suffer from degraded efficiency when waiting for straggler clients. Similarly, asynchronous federated learning algorithms experience degradation in the convergence rate and final model accuracy on non-identically and independently distributed (non-IID) heterogeneous datasets due to stale local models and client drift. To address these limitations in cross-silo federated learning with heterogeneous clients and data, we propose FedCompass, an innovative semi-asynchronous federated learning algorithm with a computing power-aware scheduler on the server side, which adaptively assigns varying amounts of training tasks to different clients using the knowledge of the computing power of individual clients. FedCompass ensures that multiple locally trained models from clients are received almost simultaneously as a group for aggregation, effectively reducing the staleness of local models. At the same time, the overall training process remains asynchronous, eliminating prolonged waiting periods from straggler clients. Using diverse non-IID heterogeneous distributed datasets, we demonstrate that FedCompass achieves faster convergence and higher accuracy than other asynchronous algorithms while remaining more efficient than synchronous algorithms when performing federated learning on heterogeneous clients. The source code for FedCompass is available at https://github.com/APPFL/FedCompass.", "authors": ["Zilinghan Li", "Pranshu Chaturvedi", "Shilan He", "Han Chen", "Gagandeep Singh", "Volodymyr Kindratenko", "E. A. Huerta", "Kibaek Kim", "Ravi Madduri"], "categories": ["cs.LG", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2023-09-26", "url": "https://arxiv.org/abs/2309.14675", "pdf_url": "https://arxiv.org/pdf/2309.14675v2", "arxiv_id": "2309.14675", "doi": "10.48550/arXiv.2309.14675", "citation_count": 23, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/APPFL/FedCompass", "venue": "International Conference on Learning Representations", "quality_score": 0.3451}
{"id": "efdaaa3d345d01dd26341c4f690aebf5287b6fe857f02ea16b139b46114a147f", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Energy-Aware Federated Traffic Prediction for Cellular Networks", "abstract": "Cellular traffic prediction is a crucial activity for optimizing networks in fifth-generation (5G) networks and beyond, as accurate forecasting is essential for intelligent network design, resource allocation and anomaly mitigation. Although machine learning (ML) is a promising approach to effectively predict network traffic, the centralization of massive data in a single data center raises issues regarding confidentiality, privacy and data transfer demands. To address these challenges, federated learning (FL) emerges as an appealing ML training framework which offers high accurate predictions through parallel distributed computations. However, the environmental impact of these methods is often overlooked, which calls into question their sustainability. In this paper, we address the trade-off between accuracy and energy consumption in FL by proposing a novel sustainability indicator that allows assessing the feasibility of ML models. Then, we comprehensively evaluate state-of-the-art deep learning (DL) architectures in a federated scenario using real-world measurements from base station (BS) sites in the area of Barcelona, Spain. Our findings indicate that larger ML models achieve marginally improved performance but have a significant environmental impact in terms of carbon footprint, which make them impractical for real-world applications.", "authors": ["Vasileios Perifanis", "Nikolaos Pavlidis", "Selim F. Yilmaz", "Francesc Wilhelmi", "Elia Guerra", "Marco Miozzo", "Pavlos S. Efraimidis", "Paolo Dini", "Remous-Aris Koutsiamanis"], "categories": ["cs.LG", "cs.AI", "cs.DC", "cs.NI"], "fields_of_study": ["Computer Science"], "published_date": "2023-09-19", "url": "https://arxiv.org/abs/2309.10645", "pdf_url": "https://arxiv.org/pdf/2309.10645v1", "arxiv_id": "2309.10645", "doi": "10.1109/FMEC59375.2023.10306017", "citation_count": 18, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "International Conference on Fog and Mobile Edge Computing", "quality_score": 0.3197}
{"id": "ba790567edca696b1eaf88f74edea291fc6bcf05fd4df99a54621082ed7284b5", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks", "abstract": "Gradient inversion attacks are an ubiquitous threat in federated learning as they exploit gradient leakage to reconstruct supposedly private training data. Recent work has proposed to prevent gradient leakage without loss of model utility by incorporating a PRivacy EnhanCing mODulE (PRECODE) based on variational modeling. Without further analysis, it was shown that PRECODE successfully protects against gradient inversion attacks. In this paper, we make multiple contributions. First, we investigate the effect of PRECODE on gradient inversion attacks to reveal its underlying working principle. We show that variational modeling introduces stochasticity into the gradients of PRECODE and the subsequent layers in a neural network. The stochastic gradients of these layers prevent iterative gradient inversion attacks from converging. Second, we formulate an attack that disables the privacy preserving effect of PRECODE by purposefully omitting stochastic gradients during attack optimization. To preserve the privacy preserving effect of PRECODE, our analysis reveals that variational modeling must be placed early in the network. However, early placement of PRECODE is typically not feasible due to reduced model utility and the exploding number of additional model parameters. Therefore, as a third contribution, we propose a novel privacy module -- the Convolutional Variational Bottleneck (CVB) -- that can be placed early in a neural network without suffering from these drawbacks. We conduct an extensive empirical study on three seminal model architectures and six image classification datasets. We find that all architectures are susceptible to gradient leakage attacks, which can be prevented by our proposed CVB. Compared to PRECODE, we show that our novel privacy module requires fewer trainable parameters, and thus computational and communication costs, to effectively preserve privacy.", "authors": ["Daniel Scheliga", "Patrick Mäder", "Marco Seeland"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2023-09-08", "url": "https://arxiv.org/abs/2309.04515", "pdf_url": "https://arxiv.org/pdf/2309.04515v1", "arxiv_id": "2309.04515", "doi": "10.1186/s42400-024-00295-9", "citation_count": 11, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Cybersecurity", "quality_score": 0.2698}
{"id": "828d74dc22ab3093f253d1b3d212b3b6a5ee44e711118c9d95d774b2f2f037e7", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory", "abstract": "With the increasing importance of data privacy protection, various privacy-preserving machine learning methods have been proposed. In the clustering domain, various algorithms with a federated learning framework (i.e., federated clustering) have been actively studied and showed high clustering performance while preserving data privacy. However, most of the base clusterers (i.e., clustering algorithms) used in existing federated clustering algorithms need to specify the number of clusters in advance. These algorithms, therefore, are unable to deal with data whose distributions are unknown or continually changing. To tackle this problem, this paper proposes a privacy-preserving continual federated clustering algorithm. In the proposed algorithm, an adaptive resonance theory-based clustering algorithm capable of continual learning is used as a base clusterer. Therefore, the proposed algorithm inherits the ability of continual learning. Experimental results with synthetic and real-world datasets show that the proposed algorithm has superior clustering performance to state-of-the-art federated clustering algorithms while realizing data privacy protection and continual learning ability. The source code is available at \\url{https://github.com/Masuyama-lab/FCAC}.", "authors": ["Naoki Masuyama", "Yusuke Nojima", "Yuichiro Toda", "Chu Kiong Loo", "Hisao Ishibuchi", "Naoyuki Kubota"], "categories": ["cs.LG", "cs.CR", "cs.NE"], "fields_of_study": ["Computer Science"], "published_date": "2023-09-07", "url": "https://arxiv.org/abs/2309.03487", "pdf_url": "https://arxiv.org/pdf/2309.03487v1", "arxiv_id": "2309.03487", "doi": "10.1109/ACCESS.2024.3467114", "citation_count": 7, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Masuyama-lab/FCAC}", "venue": "IEEE Access", "quality_score": 0.2258}
{"id": "af8720880177983dc50995445eea8aea40d1d9e9d7a7e5a3413e8de48336eea4", "sources": ["arxiv", "semantic_scholar"], "title": "Bias Propagation in Federated Learning", "abstract": "We show that participating in federated learning can be detrimental to group fairness. In fact, the bias of a few parties against under-represented groups (identified by sensitive attributes such as gender or race) can propagate through the network to all the parties in the network. We analyze and explain bias propagation in federated learning on naturally partitioned real-world datasets. Our analysis reveals that biased parties unintentionally yet stealthily encode their bias in a small number of model parameters, and throughout the training, they steadily increase the dependence of the global model on sensitive attributes. What is important to highlight is that the experienced bias in federated learning is higher than what parties would otherwise encounter in centralized training with a model trained on the union of all their data. This indicates that the bias is due to the algorithm. Our work calls for auditing group fairness in federated learning and designing learning algorithms that are robust to bias propagation.", "authors": ["Hongyan Chang", "Reza Shokri"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2023-09-05", "url": "https://arxiv.org/abs/2309.02160", "pdf_url": "https://arxiv.org/pdf/2309.02160v1", "arxiv_id": "2309.02160", "doi": "10.48550/arXiv.2309.02160", "citation_count": 24, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "International Conference on Learning Representations", "quality_score": 0.3495}
{"id": "a926787aa57d259a3d216bada16613eaeb7a07dc00153862bfdb68bd967f5433", "sources": ["arxiv", "semantic_scholar"], "title": "FedFwd: Federated Learning without Backpropagation", "abstract": "In federated learning (FL), clients with limited resources can disrupt the training efficiency. A potential solution to this problem is to leverage a new learning procedure that does not rely on backpropagation (BP). We present a novel approach to FL called FedFwd that employs a recent BP-free method by Hinton (2022), namely the Forward Forward algorithm, in the local training process. FedFwd can reduce a significant amount of computations for updating parameters by performing layer-wise local updates, and therefore, there is no need to store all intermediate activation values during training. We conduct various experiments to evaluate FedFwd on standard datasets including MNIST and CIFAR-10, and show that it works competitively to other BP-dependent FL methods.", "authors": ["Seonghwan Park", "Dahun Shin", "Jinseok Chung", "Namhoon Lee"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-09-03", "url": "https://arxiv.org/abs/2309.01150", "pdf_url": "https://arxiv.org/pdf/2309.01150v1", "arxiv_id": "2309.01150", "doi": "10.48550/arXiv.2309.01150", "citation_count": 6, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.301}
{"id": "99bf45a5a1bb45596338ffc4db97966bd6cba619e83bf65a5757023bf22a888d", "sources": ["arxiv", "semantic_scholar"], "title": "Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond", "abstract": "Federated learning (FL) is a framework for training machine learning models in a distributed and collaborative manner. During training, a set of participating clients process their data stored locally, sharing only the model updates obtained by minimizing a cost function over their local inputs. FL was proposed as a stepping-stone towards privacy-preserving machine learning, but it has been shown vulnerable to issues such as leakage of private information, lack of personalization of the model, and the possibility of having a trained model that is fairer to some groups than to others. In this paper, we address the triadic interaction among personalization, privacy guarantees, and fairness attained by models trained within the FL framework. Differential privacy and its variants have been studied and applied as cutting-edge standards for providing formal privacy guarantees. However, clients in FL often hold very diverse datasets representing heterogeneous communities, making it important to protect their sensitive information while still ensuring that the trained model upholds the aspect of fairness for the users. To attain this objective, a method is put forth that introduces group privacy assurances through the utilization of $d$-privacy (aka metric privacy). $d$-privacy represents a localized form of differential privacy that relies on a metric-oriented obfuscation approach to maintain the original data's topological distribution. This method, besides enabling personalized model training in a federated approach and providing formal privacy guarantees, possesses significantly better group fairness measured under a variety of standard metrics than a global model trained within a classical FL template. Theoretical justifications for the applicability are provided, as well as experimental validation on real-world datasets to illustrate the working of the proposed method.", "authors": ["Filippo Galli", "Kangsoo Jung", "Sayan Biswas", "Catuscia Palamidessi", "Tommaso Cucinotta"], "categories": ["cs.LG", "cs.CR", "cs.CY", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2023-09-01", "url": "https://arxiv.org/abs/2309.00416", "pdf_url": "https://arxiv.org/pdf/2309.00416v1", "arxiv_id": "2309.00416", "doi": "10.1007/s42979-023-02292-0", "citation_count": 17, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "SN Computer Science", "quality_score": 0.3138}
{"id": "4a8c9b298ddebc78a3ea56e0d8a21b0408d6ea22446bdd3e81e0c7682a973ee2", "sources": ["arxiv", "semantic_scholar"], "title": "APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service", "abstract": "Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e.g., healthcare of financial) local data. To ease and accelerate the adoption of PPFL, we introduce APPFLx, a ready-to-use platform that provides privacy-preserving cross-silo federated learning as a service. APPFLx employs Globus authentication to allow users to easily and securely invite trustworthy collaborators for PPFL, implements several synchronous and asynchronous FL algorithms, streamlines the FL experiment launch process, and enables tracking and visualizing the life cycle of FL experiments, allowing domain experts and ML practitioners to easily orchestrate and evaluate cross-silo FL under one platform. APPFLx is available online at https://appflx.link", "authors": ["Zilinghan Li", "Shilan He", "Pranshu Chaturvedi", "Trung-Hieu Hoang", "Minseok Ryu", "E. A. Huerta", "Volodymyr Kindratenko", "Jordan Fuhrman", "Maryellen Giger", "Ryan Chard", "Kibaek Kim", "Ravi Madduri"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-08-17", "url": "https://arxiv.org/abs/2308.08786", "pdf_url": "https://arxiv.org/pdf/2308.08786v1", "arxiv_id": "2308.08786", "doi": "10.1109/e-Science58273.2023.10254842", "citation_count": 17, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Conference on e-Science", "quality_score": 0.3138}
{"id": "50c53b4bacd78b7512be8af45de1c6633bb7bb8d0fcbe450214767cd21a2dc02", "sources": ["arxiv", "semantic_scholar"], "title": "Binary Federated Learning with Client-Level Differential Privacy", "abstract": "Federated learning (FL) is a privacy-preserving collaborative learning framework, and differential privacy can be applied to further enhance its privacy protection. Existing FL systems typically adopt Federated Average (FedAvg) as the training algorithm and implement differential privacy with a Gaussian mechanism. However, the inherent privacy-utility trade-off in these systems severely degrades the training performance if a tight privacy budget is enforced. Besides, the Gaussian mechanism requires model weights to be of high-precision. To improve communication efficiency and achieve a better privacy-utility trade-off, we propose a communication-efficient FL training algorithm with differential privacy guarantee. Specifically, we propose to adopt binary neural networks (BNNs) and introduce discrete noise in the FL setting. Binary model parameters are uploaded for higher communication efficiency and discrete noise is added to achieve the client-level differential privacy protection. The achieved performance guarantee is rigorously proved, and it is shown to depend on the level of discrete noise. Experimental results based on MNIST and Fashion-MNIST datasets will demonstrate that the proposed training algorithm achieves client-level privacy protection with performance gain while enjoying the benefits of low communication overhead from binary model updates.", "authors": ["Lumin Liu", "Jun Zhang", "Shenghui Song", "Khaled B. Letaief"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2023-08-07", "url": "https://arxiv.org/abs/2308.03320", "pdf_url": "https://arxiv.org/pdf/2308.03320v1", "arxiv_id": "2308.03320", "doi": "10.1109/GLOBECOM54140.2023.10437593", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Global Communications Conference", "quality_score": 0.1747}
{"id": "585d647648020d5ed2055ba52370ff865d63581194a6978423df650df553364c", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Fair and Privacy Preserving Federated Learning for the Healthcare Domain", "abstract": "Federated learning enables data sharing in healthcare contexts where it might otherwise be difficult due to data-use-ordinances or security and communication constraints. Distributed and shared data models allow models to become generalizable and learn from heterogeneous clients. While addressing data security, privacy, and vulnerability considerations, data itself is not shared across nodes in a given learning network. On the other hand, FL models often struggle with variable client data distributions and operate on an assumption of independent and identically distributed data. As the field has grown, the notion of fairness-aware federated learning mechanisms has also been introduced and is of distinct significance to the healthcare domain where many sensitive groups and protected classes exist. In this paper, we create a benchmark methodology for FAFL mechanisms under various heterogeneous conditions on datasets in the healthcare domain typically outside the scope of current federated learning benchmarks, such as medical imaging and waveform data formats. Our results indicate considerable variation in how various FAFL schemes respond to high levels of data heterogeneity. Additionally, doing so under privacy-preserving conditions can create significant increases in network communication cost and latency compared to the typical federated learning scheme.", "authors": ["Navya Annapareddy", "Yingzheng Liu", "Judy Fox"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2023-08-03", "url": "https://arxiv.org/abs/2308.01529", "pdf_url": "https://arxiv.org/pdf/2308.01529v1", "arxiv_id": "2308.01529", "doi": "10.48550/arXiv.2308.01529", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "964f307fc08223c49d0b6edafa72a91ca73d8099fe0cb9c4510325f97f1d454c", "sources": ["arxiv", "semantic_scholar"], "title": "Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation", "abstract": "Asynchronous Federated Learning with Buffered Aggregation (FedBuff) is a state-of-the-art algorithm known for its efficiency and high scalability. However, it has a high communication cost, which has not been examined with quantized communications. To tackle this problem, we present a new algorithm (QAFeL), with a quantization scheme that establishes a shared \"hidden\" state between the server and clients to avoid the error propagation caused by direct quantization. This approach allows for high precision while significantly reducing the data transmitted during client-server interactions. We provide theoretical convergence guarantees for QAFeL and corroborate our analysis with experiments on a standard benchmark.", "authors": ["Tomas Ortega", "Hamid Jafarkhani"], "categories": ["cs.LG", "eess.SP", "math.OC"], "fields_of_study": ["Computer Science", "Engineering", "Mathematics"], "published_date": "2023-08-01", "url": "https://arxiv.org/abs/2308.00263", "pdf_url": "https://arxiv.org/pdf/2308.00263v1", "arxiv_id": "2308.00263", "doi": "10.48550/arXiv.2308.00263", "citation_count": 12, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2785}
{"id": "7c3dbdd9b72645ce913794611d9e75b517a2ae98634e3a43088998b6da290592", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning for Data and Model Heterogeneity in Medical Imaging", "abstract": "Federated Learning (FL) is an evolving machine learning method in which multiple clients participate in collaborative learning without sharing their data with each other and the central server. In real-world applications such as hospitals and industries, FL counters the challenges of data heterogeneity and model heterogeneity as an inevitable part of the collaborative training. More specifically, different organizations, such as hospitals, have their own private data and customized models for local training. To the best of our knowledge, the existing methods do not effectively address both problems of model heterogeneity and data heterogeneity in FL. In this paper, we exploit the data and model heterogeneity simultaneously, and propose a method, MDH-FL (Exploiting Model and Data Heterogeneity in FL) to solve such problems to enhance the efficiency of the global model in FL. We use knowledge distillation and a symmetric loss to minimize the heterogeneity and its impact on the model performance. Knowledge distillation is used to solve the problem of model heterogeneity, and symmetric loss tackles with the data and label heterogeneity. We evaluate our method on the medical datasets to conform the real-world scenario of hospitals, and compare with the existing methods. The experimental results demonstrate the superiority of the proposed approach over the other existing methods.", "authors": ["Hussain Ahmad Madni", "Rao Muhammad Umer", "Gian Luca Foresti"], "categories": ["cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-07-31", "url": "https://arxiv.org/abs/2308.00155", "pdf_url": "https://arxiv.org/pdf/2308.00155v1", "arxiv_id": "2308.00155", "doi": "10.48550/arXiv.2308.00155", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.25}
{"id": "58ed0faf6ba73e18355b6d1ec47829188d7e3c2d88fa52a0e9cf9b68318c181d", "sources": ["arxiv", "semantic_scholar"], "title": "Blockchain-based Optimized Client Selection and Privacy Preserved Framework for Federated Learning", "abstract": "Federated learning is a distributed mechanism that trained large-scale neural network models with the participation of multiple clients and data remains on their devices, only sharing the local model updates. With this feature, federated learning is considered a secure solution for data privacy issues. However, the typical FL structure relies on the client-server, which leads to the single-point-of-failure (SPoF) attack, and the random selection of clients for model training compromised the model accuracy. Furthermore, adversaries try for inference attacks i.e., attack on privacy leads to gradient leakage attacks. We proposed the blockchain-based optimized client selection and privacy-preserved framework in this context. We designed the three kinds of smart contracts such as 1) registration of clients 2) forward bidding to select optimized clients for FL model training 3) payment settlement and reward smart contracts. Moreover, fully homomorphic encryption with Cheon, Kim, Kim, and Song (CKKS) method is implemented before transmitting the local model updates to the server. Finally, we evaluated our proposed method on the benchmark dataset and compared it with state-of-the-art studies. Consequently, we achieved a higher accuracy rate and privacy-preserved FL framework with decentralized nature.", "authors": ["Attia Qammar", "Abdenacer Naouri", "Jianguo Ding", "Huansheng Ning"], "categories": ["cs.CR", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-07-25", "url": "https://arxiv.org/abs/2308.04442", "pdf_url": "https://arxiv.org/pdf/2308.04442v1", "arxiv_id": "2308.04442", "doi": "10.48550/arXiv.2308.04442", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "11a075fe7b4d2c48fa5c63c88282924d534246b8912bd71c4f62ccefcf09ef49", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-preserving patient clustering for personalized federated learning", "abstract": "Federated Learning (FL) is a machine learning framework that enables multiple organizations to train a model without sharing their data with a central server. However, it experiences significant performance degradation if the data is non-identically independently distributed (non-IID). This is a problem in medical settings, where variations in the patient population contribute significantly to distribution differences across hospitals. Personalized FL addresses this issue by accounting for site-specific distribution differences. Clustered FL, a Personalized FL variant, was used to address this problem by clustering patients into groups across hospitals and training separate models on each group. However, privacy concerns remained as a challenge as the clustering process requires exchange of patient-level information. This was previously solved by forming clusters using aggregated data, which led to inaccurate groups and performance degradation. In this study, we propose Privacy-preserving Community-Based Federated machine Learning (PCBFL), a novel Clustered FL framework that can cluster patients using patient-level data while protecting privacy. PCBFL uses Secure Multiparty Computation, a cryptographic technique, to securely calculate patient-level similarity scores across hospitals. We then evaluate PCBFL by training a federated mortality prediction model using 20 sites from the eICU dataset. We compare the performance gain from PCBFL against traditional and existing Clustered FL frameworks. Our results show that PCBFL successfully forms clinically meaningful cohorts of low, medium, and high-risk patients. PCBFL outperforms traditional and existing Clustered FL frameworks with an average AUC improvement of 4.3% and AUPRC improvement of 7.8%.", "authors": ["Ahmed Elhussein", "Gamze Gursoy"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2023-07-17", "url": "https://arxiv.org/abs/2307.08847", "pdf_url": "https://arxiv.org/pdf/2307.08847v1", "arxiv_id": "2307.08847", "doi": "10.48550/arXiv.2307.08847", "citation_count": 16, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Machine Learning in Health Care", "quality_score": 0.3076}
{"id": "2556472dbd867cc44648919221d60b0b2a563818e944f9df2b137dbac75e922c", "sources": ["arxiv", "semantic_scholar"], "title": "FDAPT: Federated Domain-adaptive Pre-training for Language Models", "abstract": "Foundation models (FMs) have shown prominent success in a wide range of tasks. Their applicability to specific domain-task pairings relies on the availability of, both, high-quality data and significant computational resources. These challenges are not new to the field and, indeed, Federated Learning (FL) has been shown to be a promising solution in similar setups. This paper tackles the specific case of Domain-Adaptive Pre-Training (DAPT), a key step in the application of FMs. We conduct the first comprehensive empirical study to evaluate the performance of Federated Domain-Adaptive Pre-Training (FDAPT). We demonstrate that FDAPT can maintain competitive downstream task performance to the centralized baseline in both IID and non-IID situations. Finally, we propose a novel algorithm, Frozen Federated Domain-Adaptive Pre-Training (FFDAPT). FFDAPT improves the computational efficiency by 12.1% on average and exhibits similar downstream task performance to vanilla FDAPT, with general performance fluctuations remaining less than 1%.", "authors": ["Lekang Jiang", "Filip Svoboda", "Nicholas D. Lane"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2023-07-12", "url": "https://arxiv.org/abs/2307.06933", "pdf_url": "https://arxiv.org/pdf/2307.06933v2", "arxiv_id": "2307.06933", "doi": "10.48550/arXiv.2307.06933", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "8c345c14c80628295a994b7190dc622488194778aca810587a8ee56955bfab96", "sources": ["arxiv", "semantic_scholar"], "title": "Approximate, Adapt, Anonymize (3A): a Framework for Privacy Preserving Training Data Release for Machine Learning", "abstract": "The availability of large amounts of informative data is crucial for successful machine learning. However, in domains with sensitive information, the release of high-utility data which protects the privacy of individuals has proven challenging. Despite progress in differential privacy and generative modeling for privacy-preserving data release in the literature, only a few approaches optimize for machine learning utility: most approaches only take into account statistical metrics on the data itself and fail to explicitly preserve the loss metrics of machine learning models that are to be subsequently trained on the generated data. In this paper, we introduce a data release framework, 3A (Approximate, Adapt, Anonymize), to maximize data utility for machine learning, while preserving differential privacy. We also describe a specific implementation of this framework that leverages mixture models to approximate, kernel-inducing points to adapt, and Gaussian differential privacy to anonymize a dataset, in order to ensure that the resulting data is both privacy-preserving and high utility. We present experimental evidence showing minimal discrepancy between performance metrics of models trained on real versus privatized datasets, when evaluated on held-out real data. We also compare our results with several privacy-preserving synthetic data generation models (such as differentially private generative adversarial networks), and report significant increases in classification performance metrics compared to state-of-the-art models. These favorable comparisons show that the presented framework is a promising direction of research, increasing the utility of low-risk synthetic data release for machine learning.", "authors": ["Tamas Madl", "Weijie Xu", "Olivia Choudhury", "Matthew Howard"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2023-07-04", "url": "https://arxiv.org/abs/2307.01875", "pdf_url": "https://arxiv.org/pdf/2307.01875v1", "arxiv_id": "2307.01875", "doi": "10.48550/arXiv.2307.01875", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "c2a64a821807bd7c3d55256d4ff914a0b160af196a03e03e19df8049ad0b384b", "sources": ["arxiv", "semantic_scholar"], "title": "Vision Through the Veil: Differential Privacy in Federated Learning for Medical Image Classification", "abstract": "The proliferation of deep learning applications in healthcare calls for data aggregation across various institutions, a practice often associated with significant privacy concerns. This concern intensifies in medical image analysis, where privacy-preserving mechanisms are paramount due to the data being sensitive in nature. Federated learning, which enables cooperative model training without direct data exchange, presents a promising solution. Nevertheless, the inherent vulnerabilities of federated learning necessitate further privacy safeguards. This study addresses this need by integrating differential privacy, a leading privacy-preserving technique, into a federated learning framework for medical image classification. We introduce a novel differentially private federated learning model and meticulously examine its impacts on privacy preservation and model performance. Our research confirms the existence of a trade-off between model accuracy and privacy settings. However, we demonstrate that strategic calibration of the privacy budget in differential privacy can uphold robust image classification performance while providing substantial privacy protection.", "authors": ["Kishore Babu Nampalle", "Pradeep Singh", "Uppala Vivek Narayan", "Balasubramanian Raman"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2023-06-30", "url": "https://arxiv.org/abs/2306.17794", "pdf_url": "https://arxiv.org/pdf/2306.17794v1", "arxiv_id": "2306.17794", "doi": "10.48550/arXiv.2306.17794", "citation_count": 15, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.301}
{"id": "a9cfb300935a5fc8a1aed464839dfbb0a18489a1a4d35d5aa13bf451665792b3", "sources": ["arxiv", "semantic_scholar"], "title": "A Survey on Blockchain-Based Federated Learning and Data Privacy", "abstract": "Federated learning is a decentralized machine learning paradigm that allows multiple clients to collaborate by leveraging local computational power and the models transmission. This method reduces the costs and privacy concerns associated with centralized machine learning methods while ensuring data privacy by distributing training data across heterogeneous devices. On the other hand, federated learning has the drawback of data leakage due to the lack of privacy-preserving mechanisms employed during storage, transfer, and sharing, thus posing significant risks to data owners and suppliers. Blockchain technology has emerged as a promising technology for offering secure data-sharing platforms in federated learning, especially in Industrial Internet of Things (IIoT) settings. This survey aims to compare the performance and security of various data privacy mechanisms adopted in blockchain-based federated learning architectures. We conduct a systematic review of existing literature on secure data-sharing platforms for federated learning provided by blockchain technology, providing an in-depth overview of blockchain-based federated learning, its essential components, and discussing its principles, and potential applications. The primary contribution of this survey paper is to identify critical research questions and propose potential directions for future research in blockchain-based federated learning.", "authors": ["Bipin Chhetri", "Saroj Gopali", "Rukayat Olapojoye", "Samin Dehbash", "Akbar Siami Namin"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-06-29", "url": "https://arxiv.org/abs/2306.17338", "pdf_url": "https://arxiv.org/pdf/2306.17338v1", "arxiv_id": "2306.17338", "doi": "10.1109/COMPSAC57700.2023.00199", "citation_count": 26, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Annual International Computer Software and Applications Conference", "quality_score": 0.3578}
{"id": "18fd72f9227df3c45b244ebc7c19e6ab8a2d323c8c347455cb1d6b4aa3335226", "sources": ["arxiv", "semantic_scholar"], "title": "Differentially Private Distributed Estimation and Learning", "abstract": "We study distributed estimation and learning problems in a networked environment where agents exchange information to estimate unknown statistical properties of random variables from their privately observed samples. The agents can collectively estimate the unknown quantities by exchanging information about their private observations, but they also face privacy risks. Our novel algorithms extend the existing distributed estimation literature and enable the participating agents to estimate a complete sufficient statistic from private signals acquired offline or online over time and to preserve the privacy of their signals and network neighborhoods. This is achieved through linear aggregation schemes with adjusted randomization schemes that add noise to the exchanged estimates subject to differential privacy (DP) constraints, both in an offline and online manner. We provide convergence rate analysis and tight finite-time convergence bounds. We show that the noise that minimizes the convergence time to the best estimates is the Laplace noise, with parameters corresponding to each agent's sensitivity to their signal and network characteristics. Our algorithms are amenable to dynamic topologies and balancing privacy and accuracy trade-offs. Finally, to supplement and validate our theoretical results, we run experiments on real-world data from the US Power Grid Network and electric consumption data from German Households to estimate the average power consumption of power stations and households under all privacy regimes and show that our method outperforms existing first-order, privacy-aware, distributed optimization methods.", "authors": ["Marios Papachristou", "M. Amin Rahimian"], "categories": ["cs.LG", "cs.SI", "eess.SY", "math.ST", "stat.AP", "stat.ML"], "fields_of_study": ["Computer Science", "Engineering", "Mathematics"], "published_date": "2023-06-28", "url": "https://arxiv.org/abs/2306.15865", "pdf_url": "https://arxiv.org/pdf/2306.15865v5", "arxiv_id": "2306.15865", "doi": "10.1080/24725854.2024.2337068", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IISE Transactions", "quality_score": 0.1193}
{"id": "59463ac508e14ef1cd259dc693f9f73c1e60bb8eb76b8d147eac02af412247fd", "sources": ["arxiv", "semantic_scholar"], "title": "Medical Federated Model with Mixture of Personalized and Sharing Components", "abstract": "Although data-driven methods usually have noticeable performance on disease diagnosis and treatment, they are suspected of leakage of privacy due to collecting data for model training. Recently, federated learning provides a secure and trustable alternative to collaboratively train model without any exchange of medical data among multiple institutes. Therefore, it has draw much attention due to its natural merit on privacy protection. However, when heterogenous medical data exists between different hospitals, federated learning usually has to face with degradation of performance. In the paper, we propose a new personalized framework of federated learning to handle the problem. It successfully yields personalized models based on awareness of similarity between local data, and achieves better tradeoff between generalization and personalization than existing methods. After that, we further design a differentially sparse regularizer to improve communication efficiency during procedure of model training. Additionally, we propose an effective method to reduce the computational cost, which improves computation efficiency significantly. Furthermore, we collect 5 real medical datasets, including 2 public medical image datasets and 3 private multi-center clinical diagnosis datasets, and evaluate its performance by conducting nodule classification, tumor segmentation, and clinical risk prediction tasks. Comparing with 13 existing related methods, the proposed method successfully achieves the best model performance, and meanwhile up to 60% improvement of communication efficiency. Source code is public, and can be accessed at: https://github.com/ApplicationTechnologyOfMedicalBigData/pFedNet-code.", "authors": ["Yawei Zhao", "Qinghe Liu", "Xinwang Liu", "Kunlun He"], "categories": ["cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-06-26", "url": "https://arxiv.org/abs/2306.14483", "pdf_url": "https://arxiv.org/pdf/2306.14483v1", "arxiv_id": "2306.14483", "doi": "10.48550/arXiv.2306.14483", "citation_count": 5, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/ApplicationTechnologyOfMedicalBigData/pFedNet-code", "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "6add2169a6291f32554c290c1fb98105478281303b76382f9daf4e306cf23e9f", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Quantum Federated Learning", "abstract": "Quantum Federated Learning (QFL) is an emerging interdisciplinary field that merges the principles of Quantum Computing (QC) and Federated Learning (FL), with the goal of leveraging quantum technologies to enhance privacy, security, and efficiency in the learning process. Currently, there is no comprehensive survey for this interdisciplinary field. This review offers a thorough, holistic examination of QFL. We aim to provide a comprehensive understanding of the principles, techniques, and emerging applications of QFL. We discuss the current state of research in this rapidly evolving field, identify challenges and opportunities associated with integrating these technologies, and outline future directions and open research questions. We propose a unique taxonomy of QFL techniques, categorized according to their characteristics and the quantum techniques employed. As the field of QFL continues to progress, we can anticipate further breakthroughs and applications across various industries, driving innovation and addressing challenges related to data privacy, security, and resource optimization. This review serves as a first-of-its-kind comprehensive guide for researchers and practitioners interested in understanding and advancing the field of QFL.", "authors": ["Chao Ren", "Rudai Yan", "Huihui Zhu", "Han Yu", "Minrui Xu", "Yuan Shen", "Yan Xu", "Ming Xiao", "Zhao Yang Dong", "Mikael Skoglund", "Dusit Niyato", "Leong Chuan Kwek"], "categories": ["cs.LG", "quant-ph"], "fields_of_study": ["Computer Science", "Physics"], "published_date": "2023-06-16", "url": "https://arxiv.org/abs/2306.09912", "pdf_url": "https://arxiv.org/pdf/2306.09912v4", "arxiv_id": "2306.09912", "doi": "10.48550/arXiv.2306.09912", "citation_count": 35, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3891}
{"id": "8f4414860074f5a3b36486d68e290fa03557d8e80c53fe4bae59d03de544aa6d", "sources": ["arxiv", "semantic_scholar"], "title": "Fairness and Privacy-Preserving in Federated Learning: A Survey", "abstract": "Federated learning (FL) as distributed machine learning has gained popularity as privacy-aware Machine Learning (ML) systems have emerged as a technique that prevents privacy leakage by building a global model and by conducting individualized training of decentralized edge clients on their own private data. The existing works, however, employ privacy mechanisms such as Secure Multiparty Computing (SMC), Differential Privacy (DP), etc. Which are immensely susceptible to interference, massive computational overhead, low accuracy, etc. With the increasingly broad deployment of FL systems, it is challenging to ensure fairness and maintain active client participation in FL systems. Very few works ensure reasonably satisfactory performances for the numerous diverse clients and fail to prevent potential bias against particular demographics in FL systems. The current efforts fail to strike a compromise between privacy, fairness, and model performance in FL systems and are vulnerable to a number of additional problems. In this paper, we provide a comprehensive survey stating the basic concepts of FL, the existing privacy challenges, techniques, and relevant works concerning privacy in FL. We also provide an extensive overview of the increasing fairness challenges, existing fairness notions, and the limited works that attempt both privacy and fairness in FL. By comprehensively describing the existing FL systems, we present the potential future directions pertaining to the challenges of privacy-preserving and fairness-aware FL systems.", "authors": ["Taki Hasan Rafi", "Faiza Anan Noor", "Tahmid Hussain", "Dong-Kyu Chae"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2023-06-14", "url": "https://arxiv.org/abs/2306.08402", "pdf_url": "https://arxiv.org/pdf/2306.08402v2", "arxiv_id": "2306.08402", "doi": "10.48550/arXiv.2306.08402", "citation_count": 98, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "Information Fusion", "quality_score": 0.4989}
{"id": "7a30398a38b3e0eb6a4f26efe1a5113af9535755d7684f12a9c9699498338a19", "sources": ["arxiv", "semantic_scholar"], "title": "Personalized Graph Federated Learning with Differential Privacy", "abstract": "This paper presents a personalized graph federated learning (PGFL) framework in which distributedly connected servers and their respective edge devices collaboratively learn device or cluster-specific models while maintaining the privacy of every individual device. The proposed approach exploits similarities among different models to provide a more relevant experience for each device, even in situations with diverse data distributions and disproportionate datasets. Furthermore, to ensure a secure and efficient approach to collaborative personalized learning, we study a variant of the PGFL implementation that utilizes differential privacy, specifically zero-concentrated differential privacy, where a noise sequence perturbs model exchanges. Our mathematical analysis shows that the proposed privacy-preserving PGFL algorithm converges to the optimal cluster-specific solution for each cluster in linear time. It also shows that exploiting similarities among clusters leads to an alternative output whose distance to the original solution is bounded, and that this bound can be adjusted by modifying the algorithm's hyperparameters. Further, our analysis shows that the algorithm ensures local differential privacy for all clients in terms of zero-concentrated differential privacy. Finally, the performance of the proposed PGFL algorithm is examined by performing numerical experiments in the context of regression and classification using synthetic data and the MNIST dataset.", "authors": ["Francois Gauthier", "Vinay Chakravarthi Gogineni", "Stefan Werner", "Yih-Fang Huang", "Anthony Kuh"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2023-06-10", "url": "https://arxiv.org/abs/2306.06399", "pdf_url": "https://arxiv.org/pdf/2306.06399v1", "arxiv_id": "2306.06399", "doi": "10.1109/TSIPN.2023.3325963", "citation_count": 18, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Signal and Information Processing over Networks", "quality_score": 0.3197}
{"id": "66bbdaf38cf3864fec49d297eddc65a730db140aec0c41a0b844d05caf0bfb57", "sources": ["arxiv", "semantic_scholar"], "title": "A Privacy-Preserving Federated Learning Approach for Kernel methods", "abstract": "It is challenging to implement Kernel methods, if the data sources are distributed and cannot be joined at a trusted third party for privacy reasons. It is even more challenging, if the use case rules out privacy-preserving approaches that introduce noise. An example for such a use case is machine learning on clinical data. To realize exact privacy preserving computation of kernel methods, we propose FLAKE, a Federated Learning Approach for KErnel methods on horizontally distributed data. With FLAKE, the data sources mask their data so that a centralized instance can compute a Gram matrix without compromising privacy. The Gram matrix allows to calculate many kernel matrices, which can be used to train kernel-based machine learning algorithms such as Support Vector Machines. We prove that FLAKE prevents an adversary from learning the input data or the number of input features under a semi-honest threat model. Experiments on clinical and synthetic data confirm that FLAKE is outperforming the accuracy and efficiency of comparable methods. The time needed to mask the data and to compute the Gram matrix is several orders of magnitude less than the time a Support Vector Machine needs to be trained. Thus, FLAKE can be applied to many use cases.", "authors": ["Anika Hannemann", "Ali Burak Ünal", "Arjhun Swaminathan", "Erik Buchmann", "Mete Akgün"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2023-06-05", "url": "https://arxiv.org/abs/2306.02677", "pdf_url": "https://arxiv.org/pdf/2306.02677v1", "arxiv_id": "2306.02677", "doi": "10.1109/TPS-ISA58951.2023.00020", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Trust, Privacy and Security in Intelligent Systems and Applications", "quality_score": 0.2258}
{"id": "61f95cd49e4c25329346847baa5e4c5a790c0a6da6d71fdb32c7b3230fab84cb", "sources": ["arxiv", "semantic_scholar"], "title": "Forgettable Federated Linear Learning with Certified Data Unlearning", "abstract": "Federated Learning (FL) enables collaborative model training across distributed clients while preserving user privacy. Recently, Federated Unlearning (FU) has emerged to address the \"right to be forgotten\" and to remove the influence of poisoned or target clients without retraining the entire FL system. However, many FU methods require communication with retained or target clients, introduce additional security risks, or store historical models, limiting their efficiency and practicality. Moreover, most FU methods for deep neural networks (DNNs) lack theoretical certification due to the complexity of nonlinear models and their training dynamics. In this work, we introduce Forgettable Federated Linear Learning, a training and unlearning framework for DNNs. Our approach uses pre-trained models to linearly approximate DNNs and achieve performance comparable to the original networks through Federated Linear Training. We further present a certified, efficient, and secure unlearning strategy that enables the server to remove a target client's influence without additional client communication or storage. Extensive experiments on small- to large-scale datasets, using both convolutional neural networks and modern foundation models, show that our method balances model accuracy with effective target-client unlearning. This work provides a practical pipeline for efficient and trustworthy FU. Code: https://github.com/Nanboy-Ronan/2F2L-Federated-Unlearning", "authors": ["Ruinan Jin", "Minghui Chen", "Qiong Zhang", "Xiaoxiao Li"], "categories": ["cs.LG", "cs.CV"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2023-06-03", "url": "https://arxiv.org/abs/2306.02216", "pdf_url": "https://arxiv.org/pdf/2306.02216v3", "arxiv_id": "2306.02216", "doi": "10.1109/TNNLS.2026.3683398", "citation_count": 7, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Nanboy-Ronan/2F2L-Federated-Unlearning", "venue": "IEEE Transactions on Neural Networks and Learning Systems", "quality_score": 0.2258}
{"id": "d8f28064ac1a79646341d4ba517c02ecfbef6526035bcfb40bc12f756877e3a5", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Model Aggregation for Asynchronous Federated Learning", "abstract": "We present a novel privacy-preserving model aggregation for asynchronous federated learning, named PPA-AFL that removes the restriction of synchronous aggregation of local model updates in federated learning, while enabling the protection of the local model updates against the server. In PPA-AFL, clients can proactive decide when to engage in the training process, and sends local model updates to the server when the updates are available. Thus, it is not necessary to keep synchronicity with other clients. To safeguard client updates and facilitate local model aggregation, we employ Paillier encryption for local update encryption and support homomorphic aggregation. Furthermore, secret sharing is utilized to enable the sharing of decryption keys and facilitate privacy-preserving asynchronous aggregation. As a result, the server remains unable to gain any information about the local updates while asynchronously aggregating to produce the global model. We demonstrate the efficacy of our proposed PPA-AFL framework through comprehensive complexity analysis and extensive experiments on a prototype implementation, highlighting its potential for practical adoption in privacy-sensitive asynchronous federated learning scenarios.", "authors": ["Jianxiang Zhao", "Xiangman Li", "Jianbing Ni"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2023-05-27", "url": "https://arxiv.org/abs/2305.17521", "pdf_url": "https://arxiv.org/pdf/2305.17521v1", "arxiv_id": "2305.17521", "doi": "10.1109/ICCC57788.2023.10233295", "citation_count": 2, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1505}
{"id": "001c06c586e8f9b9af0d5970a1eb983f9f85f3f4b716f2f254412f1b45af60f9", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models", "abstract": "Foundation Models (FMs), such as LLaMA, BERT, GPT, ViT, and CLIP, have demonstrated remarkable success in a wide range of applications, driven by their ability to leverage vast amounts of data for pre-training. However, optimizing FMs often requires access to sensitive data, raising privacy concerns and limiting their applicability in many domains. In this paper, we propose the Federated Foundation Models (FFMs) paradigm, which combines the benefits of FMs and Federated Learning (FL) to enable privacy-preserving and collaborative learning across multiple end-users. We discuss the potential benefits and challenges of integrating FL into the lifespan of FMs, covering pre-training, fine-tuning, and application. We further outline potential future research avenues in FFM, including FFM pre-training, FFM fine-tuning, and federated prompt tuning, which allow the development of more personalized and context-aware models while ensuring data privacy. Moreover, we explore the possibility of continual/lifelong learning in FFMs, as increased computational power at the edge may unlock the potential for optimizing FMs using newly generated private data close to the data source. The proposed FFM concepts offer a flexible and scalable framework for training large language models in a privacy-preserving manner, setting the stage for subsequent advancements in both FM training and federated learning.", "authors": ["Sixing Yu", "J. Pablo Muñoz", "Ali Jannesari"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2023-05-19", "url": "https://arxiv.org/abs/2305.11414", "pdf_url": "https://arxiv.org/pdf/2305.11414v3", "arxiv_id": "2305.11414", "doi": "10.48550/arXiv.2305.11414", "citation_count": 76, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "International Conference on Language Resources and Evaluation", "quality_score": 0.4716}
{"id": "939d21badd6715c3328e933bfae3e4f0de4698901bb1eedd55c5f1ef80a74392", "sources": ["arxiv", "semantic_scholar"], "title": "Efficient Vertical Federated Learning with Secure Aggregation", "abstract": "The majority of work in privacy-preserving federated learning (FL) has been focusing on horizontally partitioned datasets where clients share the same sets of features and can train complete models independently. However, in many interesting problems, such as financial fraud detection and disease detection, individual data points are scattered across different clients/organizations in vertical federated learning. Solutions for this type of FL require the exchange of gradients between participants and rarely consider privacy and security concerns, posing a potential risk of privacy leakage. In this work, we present a novel design for training vertical FL securely and efficiently using state-of-the-art security modules for secure aggregation. We demonstrate empirically that our method does not impact training performance whilst obtaining 9.1e2 ~3.8e4 speedup compared to homomorphic encryption (HE).", "authors": ["Xinchi Qiu", "Heng Pan", "Wanru Zhao", "Chenyang Ma", "Pedro Porto Buarque de Gusmão", "Nicholas D. Lane"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2023-05-18", "url": "https://arxiv.org/abs/2305.11236", "pdf_url": "https://arxiv.org/pdf/2305.11236v1", "arxiv_id": "2305.11236", "doi": "10.48550/arXiv.2305.11236", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2113}
{"id": "4dbeedd6e5b4a89c200ca4afad9b337ed039a4ee6d92cc4f67d1850032837765", "sources": ["arxiv", "semantic_scholar"], "title": "MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning", "abstract": "With the growth of computer vision applications, deep learning, and edge computing contribute to ensuring practical collaborative intelligence (CI) by distributing the workload among edge devices and the cloud. However, running separate single-task models on edge devices is inefficient regarding the required computational resource and time. In this context, multi-task learning allows leveraging a single deep learning model for performing multiple tasks, such as semantic segmentation and depth estimation on incoming video frames. This single processing pipeline generates common deep features that are shared among multi-task modules. However, in a collaborative intelligence scenario, generating common deep features has two major issues. First, the deep features may inadvertently contain input information exposed to the downstream modules (violating input privacy). Second, the generated universal features expose a piece of collective information than what is intended for a certain task, in which features for one task can be utilized to perform another task (violating task privacy). This paper proposes a novel deep learning-based privacy-cognizant feature generation process called MetaMorphosis that limits inference capability to specific tasks at hand. To achieve this, we propose a channel squeeze-excitation based feature metamorphosis module, Cross-SEC, to achieve distinct attention of all tasks and a de-correlation loss function with differential-privacy to train a deep learning model that produces distinct privacy-aware features as an output for the respective tasks. With extensive experimentation on four datasets consisting of diverse images related to scene understanding and facial attributes, we show that MetaMorphosis outperforms recent adversarial learning and universal feature generation methods by guaranteeing privacy requirements in an efficient way for image and video analytics.", "authors": ["Md Adnan Arefeen", "Zhouyu Li", "Md Yusuf Sarwar Uddin", "Anupam Das"], "categories": ["cs.CV", "cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2023-05-13", "url": "https://arxiv.org/abs/2305.07815", "pdf_url": "https://arxiv.org/pdf/2305.07815v1", "arxiv_id": "2305.07815", "doi": "10.1145/3576842.3582372", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Internet-of-Things Design and Implementation", "quality_score": 0.0}
{"id": "0967366e706f3b5d298bbf3b993d9712056e35c2694153b09fc7d79cbff7d4f7", "sources": ["arxiv", "semantic_scholar"], "title": "MISO: Legacy-compatible Privacy-preserving Single Sign-on using Trusted Execution Environments", "abstract": "Single sign-on (SSO) allows users to authenticate to third-party applications through a central identity provider. Despite their wide adoption, deployed SSO systems suffer from privacy problems such as user tracking by the identity provider. While numerous solutions have been proposed by academic papers, none were adopted because they require modifying identity providers, a significant adoption barrier in practice. Solutions do get deployed, however, fail to eliminate major privacy issues. Leveraging Trusted Execution Environments (TEEs), we propose MISO, the first privacy-preserving SSO system that is completely compatible with existing identity providers (such as Google and Facebook). This means MISO can be easily integrated into existing SSO ecosystem today and benefit end users. MISO also enables new functionality that standard SSO cannot offer: MISO allows users to leverage multiple identity providers in a single SSO workflow, potentially in a threshold fashion, to better protect user accounts. We fully implemented MISO based on Intel SGX. Our evaluation shows that MISO can handle high user concurrency with practical performance.", "authors": ["Rongwu Xu", "Sen Yang", "Fan Zhang", "Zhixuan Fang"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2023-05-11", "url": "https://arxiv.org/abs/2305.06833", "pdf_url": "https://arxiv.org/pdf/2305.06833v2", "arxiv_id": "2305.06833", "doi": "10.1109/EuroSP57164.2023.00029", "citation_count": 10, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "European Symposium on Security and Privacy", "quality_score": 0.2603}
{"id": "8517271064f55685bb5c11b69b3e8f784df34b69e7a669b1c6f3a7f12b9c136b", "sources": ["arxiv", "semantic_scholar"], "title": "Turning Privacy-preserving Mechanisms against Federated Learning", "abstract": "Recently, researchers have successfully employed Graph Neural Networks (GNNs) to build enhanced recommender systems due to their capability to learn patterns from the interaction between involved entities. In addition, previous studies have investigated federated learning as the main solution to enable a native privacy-preserving mechanism for the construction of global GNN models without collecting sensitive data into a single computation unit. Still, privacy issues may arise as the analysis of local model updates produced by the federated clients can return information related to sensitive local data. For this reason, experts proposed solutions that combine federated learning with Differential Privacy strategies and community-driven approaches, which involve combining data from neighbor clients to make the individual local updates less dependent on local sensitive data. In this paper, we identify a crucial security flaw in such a configuration, and we design an attack capable of deceiving state-of-the-art defenses for federated learning. The proposed attack includes two operating modes, the first one focusing on convergence inhibition (Adversarial Mode), and the second one aiming at building a deceptive rating injection on the global federated model (Backdoor Mode). The experimental results show the effectiveness of our attack in both its modes, returning on average 60% performance detriment in all the tests on Adversarial Mode and fully effective backdoors in 93% of cases for the tests performed on Backdoor Mode.", "authors": ["Marco Arazzi", "Mauro Conti", "Antonino Nocera", "Stjepan Picek"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2023-05-09", "url": "https://arxiv.org/abs/2305.05355", "pdf_url": "https://arxiv.org/pdf/2305.05355v1", "arxiv_id": "2305.05355", "doi": "10.1145/3576915.3623114", "citation_count": 21, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Conference on Computer and Communications Security", "quality_score": 0.3356}
{"id": "5074a7bd72b62b56873696c5861542c9b3b223ea31583ad30a9a13c9077dc539", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion", "abstract": "Federated learning (FL) enables participating parties to collaboratively build a global model with boosted utility without disclosing private data information. Appropriate protection mechanisms have to be adopted to fulfill the requirements in preserving \\textit{privacy} and maintaining high model \\textit{utility}. The nature of the widely-adopted protection mechanisms including \\textit{Randomization Mechanism} and \\textit{Compression Mechanism} is to protect privacy via distorting model parameter. We measure the utility via the gap between the original model parameter and the distorted model parameter. We want to identify under what general conditions privacy-preserving federated learning can achieve near-optimal utility via data generation and parameter distortion. To provide an avenue for achieving near-optimal utility, we present an upper bound for utility loss, which is measured using two main terms called variance-reduction and model parameter discrepancy separately. Our analysis inspires the design of appropriate protection parameters for the protection mechanisms to achieve near-optimal utility and meet the privacy requirements simultaneously. The main techniques for the protection mechanism include parameter distortion and data generation, which are generic and can be applied extensively. Furthermore, we provide an upper bound for the trade-off between privacy and utility, \\blue{which together with the lower bound provided by no free lunch theorem in federated learning (\\cite{zhang2022no}) form the conditions for achieving optimal trade-off.", "authors": ["Xiaojin Zhang", "Kai Chen", "Qiang Yang"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2023-05-07", "url": "https://arxiv.org/abs/2305.04288", "pdf_url": "https://arxiv.org/pdf/2305.04288v3", "arxiv_id": "2305.04288", "doi": "10.48550/arXiv.2305.04288", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2113}
{"id": "4b4e0d82821f877625bacba7dd3c58e394ab5cedf4bf009a12aeea6609d39b6f", "sources": ["arxiv", "semantic_scholar"], "title": "Personalized Federated Learning under Mixture of Distributions", "abstract": "The recent trend towards Personalized Federated Learning (PFL) has garnered significant attention as it allows for the training of models that are tailored to each client while maintaining data privacy. However, current PFL techniques primarily focus on modeling the conditional distribution heterogeneity (i.e. concept shift), which can result in suboptimal performance when the distribution of input data across clients diverges (i.e. covariate shift). Additionally, these techniques often lack the ability to adapt to unseen data, further limiting their effectiveness in real-world scenarios. To address these limitations, we propose a novel approach, FedGMM, which utilizes Gaussian mixture models (GMM) to effectively fit the input data distributions across diverse clients. The model parameters are estimated by maximum likelihood estimation utilizing a federated Expectation-Maximization algorithm, which is solved in closed form and does not assume gradient similarity. Furthermore, FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.", "authors": ["Yue Wu", "Shuaicheng Zhang", "Wenchao Yu", "Yanchi Liu", "Quanquan Gu", "Dawei Zhou", "Haifeng Chen", "Wei Cheng"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-05-01", "url": "https://arxiv.org/abs/2305.01068", "pdf_url": "https://arxiv.org/pdf/2305.01068v1", "arxiv_id": "2305.01068", "doi": "10.48550/arXiv.2305.01068", "citation_count": 73, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "International Conference on Machine Learning", "quality_score": 0.4673}
{"id": "5c06095ccbd931c0d5c5e3e18fc17e634cc926f21e3bbe5ac4fe36a77d25b79a", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving CNN Training with Transfer Learning: Multiclass Logistic Regression", "abstract": "In this paper, we present a practical solution to implement privacy-preserving CNN training based on mere Homomorphic Encryption (HE) technique. To our best knowledge, this is the first attempt successfully to crack this nut and no work ever before has achieved this goal. Several techniques combine to accomplish the task:: (1) with transfer learning, privacy-preserving CNN training can be reduced to homomorphic neural network training, or even multiclass logistic regression (MLR) training; (2) via a faster gradient variant called $\\texttt{Quadratic Gradient}$, an enhanced gradient method for MLR with a state-of-the-art performance in convergence speed is applied in this work to achieve high performance; (3) we employ the thought of transformation in mathematics to transform approximating Softmax function in the encryption domain to the approximation of the Sigmoid function. A new type of loss function termed $\\texttt{Squared Likelihood Error}$ has been developed alongside to align with this change.; and (4) we use a simple but flexible matrix-encoding method named $\\texttt{Volley Revolver}$ to manage the data flow in the ciphertexts, which is the key factor to complete the whole homomorphic CNN training. The complete, runnable C++ code to implement our work can be found at: \\href{https://github.com/petitioner/HE.CNNtraining}{$\\texttt{https://github.com/petitioner/HE.CNNtraining}$}. We select $\\texttt{REGNET\\_X\\_400MF}$ as our pre-trained model for transfer learning. We use the first 128 MNIST training images as training data and the whole MNIST testing dataset as the testing data. The client only needs to upload 6 ciphertexts to the cloud and it takes $\\sim 21$ mins to perform 2 iterations on a cloud with 64 vCPUs, resulting in a precision of $21.49\\%$.", "authors": ["John Chiang"], "categories": ["cs.CR", "cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-04-07", "url": "https://arxiv.org/abs/2304.03807", "pdf_url": "https://arxiv.org/pdf/2304.03807v5", "arxiv_id": "2304.03807", "doi": "10.48550/arXiv.2304.03807", "citation_count": 8, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/petitioner/HE.CNNtraining}{$\\texttt{https://github.com/petitioner/HE.CNNtraining}$}", "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "214c60fa22a21a9abc34e66c41351bcac635672c1b4fe76d347060fb3d13bc91", "sources": ["arxiv", "semantic_scholar"], "title": "FedBot: Enhancing Privacy in Chatbots with Federated Learning", "abstract": "Chatbots are mainly data-driven and usually based on utterances that might be sensitive. However, training deep learning models on shared data can violate user privacy. Such issues have commonly existed in chatbots since their inception. In the literature, there have been many approaches to deal with privacy, such as differential privacy and secure multi-party computation, but most of them need to have access to users' data. In this context, Federated Learning (FL) aims to protect data privacy through distributed learning methods that keep the data in its location. This paper presents Fedbot, a proof-of-concept (POC) privacy-preserving chatbot that leverages large-scale customer support data. The POC combines Deep Bidirectional Transformer models and federated learning algorithms to protect customer data privacy during collaborative model training. The results of the proof-of-concept showcase the potential for privacy-preserving chatbots to transform the customer support industry by delivering personalized and efficient customer service that meets data privacy regulations and legal requirements. Furthermore, the system is specifically designed to improve its performance and accuracy over time by leveraging its ability to learn from previous interactions.", "authors": ["Addi Ait-Mlouk", "Sadi Alawadi", "Salman Toor", "Andreas Hellander"], "categories": ["cs.CL", "cs.AI", "cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-04-04", "url": "https://arxiv.org/abs/2304.03228", "pdf_url": "https://arxiv.org/pdf/2304.03228v1", "arxiv_id": "2304.03228", "doi": "10.48550/arXiv.2304.03228", "citation_count": 8, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "53e8bfe898b34045a43d413c33508d538277703508d1f90e34186be71c980a74", "sources": ["arxiv", "semantic_scholar"], "title": "Scalable and Privacy-Preserving Federated Principal Component Analysis", "abstract": "Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data confidentiality. Our solution, SF-PCA, is an end-to-end secure system that preserves the confidentiality of both the original data and all intermediate results in a passive-adversary model with up to all-but-one colluding parties. SF-PCA jointly leverages multiparty homomorphic encryption, interactive protocols, and edge computing to efficiently interleave computations on local cleartext data with operations on collectively encrypted data. SF-PCA obtains results as accurate as non-secure centralized solutions, independently of the data distribution among the parties. It scales linearly or better with the dataset dimensions and with the number of data providers. SF-PCA is more precise than existing approaches that approximate the solution by combining local analysis results, and between 3x and 250x faster than privacy-preserving alternatives based solely on secure multiparty computation or homomorphic encryption. Our work demonstrates the practical applicability of secure and federated PCA on private distributed datasets.", "authors": ["David Froelicher", "Hyunghoon Cho", "Manaswitha Edupalli", "Joao Sa Sousa", "Jean-Philippe Bossuat", "Apostolos Pyrgelis", "Juan R. Troncoso-Pastoriza", "Bonnie Berger", "Jean-Pierre Hubaux"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2023-03-31", "url": "https://arxiv.org/abs/2304.00129", "pdf_url": "https://arxiv.org/pdf/2304.00129v1", "arxiv_id": "2304.00129", "doi": "10.1109/SP46215.2023.00051", "citation_count": 30, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "IEEE Symposium on Security and Privacy", "quality_score": 0.3728}
{"id": "e28750862dc20224f1520a8f6f9e7cd55120e90e37588aecef3cff78d53c3dc6", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-preserving machine learning for healthcare: open challenges and future perspectives", "abstract": "Machine Learning (ML) has recently shown tremendous success in modeling various healthcare prediction tasks, ranging from disease diagnosis and prognosis to patient treatment. Due to the sensitive nature of medical data, privacy must be considered along the entire ML pipeline, from model training to inference. In this paper, we conduct a review of recent literature concerning Privacy-Preserving Machine Learning (PPML) for healthcare. We primarily focus on privacy-preserving training and inference-as-a-service, and perform a comprehensive review of existing trends, identify challenges, and discuss opportunities for future research directions. The aim of this review is to guide the development of private and efficient ML models in healthcare, with the prospects of translating research efforts into real-world settings.", "authors": ["Alejandro Guerra-Manzanares", "L. Julian Lechuga Lopez", "Michail Maniatakos", "Farah E. Shamout"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2023-03-27", "url": "https://arxiv.org/abs/2303.15563", "pdf_url": "https://arxiv.org/pdf/2303.15563v1", "arxiv_id": "2303.15563", "doi": "10.1007/978-3-031-39539-0_3", "citation_count": 25, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "Trustworthy Machine Learning for Healthcare. TML4H 2023. Lecture Notes in Computer Science, vol 13932", "quality_score": 0.3537}
{"id": "234a55461e74a03cdd24cf7d2d16822e23d4d8a836061ba24c4302ebfa565eca", "sources": ["arxiv", "semantic_scholar"], "title": "FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System", "abstract": "Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data. However, privacy concerns arise as the aggregated local models on the server may reveal sensitive personal information by inversion attacks. Privacy-preserving methods, such as homomorphic encryption (HE), then become necessary for FL training. Despite HE's privacy advantages, its applications suffer from impractical overheads, especially for foundation models. In this paper, we present FedML-HE, the first practical federated learning system with efficient HE-based secure model aggregation. FedML-HE proposes to selectively encrypt sensitive parameters, significantly reducing both computation and communication overheads during training while providing customizable privacy preservation. Our optimized system demonstrates considerable overhead reduction, particularly for large foundation models (e.g., ~10x reduction for ResNet-50, and up to ~40x reduction for BERT), demonstrating the potential for scalable HE-based FL deployment.", "authors": ["Weizhao Jin", "Yuhang Yao", "Shanshan Han", "Jiajun Gu", "Carlee Joe-Wong", "Srivatsan Ravi", "Salman Avestimehr", "Chaoyang He"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2023-03-20", "url": "https://arxiv.org/abs/2303.10837", "pdf_url": "https://arxiv.org/pdf/2303.10837v3", "arxiv_id": "2303.10837", "doi": "10.48550/arXiv.2303.10837", "citation_count": 131, "influential_citation_count": 8, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5301}
{"id": "d75ce1e7ae0f647a3dae5ac640042051d04e197c585b636e1303999c68b0b5d0", "sources": ["arxiv", "semantic_scholar"], "title": "A Privacy Preserving System for Movie Recommendations Using Federated Learning", "abstract": "Recommender systems have become ubiquitous in the past years. They solve the tyranny of choice problem faced by many users, and are utilized by many online businesses to drive engagement and sales. Besides other criticisms, like creating filter bubbles within social networks, recommender systems are often reproved for collecting considerable amounts of personal data. However, to personalize recommendations, personal information is fundamentally required. A recent distributed learning scheme called federated learning has made it possible to learn from personal user data without its central collection. Consequently, we present a recommender system for movie recommendations, which provides privacy and thus trustworthiness on multiple levels: First and foremost, it is trained using federated learning and thus, by its very nature, privacy-preserving, while still enabling users to benefit from global insights. Furthermore, a novel federated learning scheme, called FedQ, is employed, which not only addresses the problem of non-i.i.d.-ness and small local datasets, but also prevents input data reconstruction attacks by aggregating client updates early. Finally, to reduce the communication overhead, compression is applied, which significantly compresses the exchanged neural network parametrizations to a fraction of their original size. We conjecture that this may also improve data privacy through its lossy quantization stage.", "authors": ["David Neumann", "Andreas Lutz", "Karsten Müller", "Wojciech Samek"], "categories": ["cs.IR", "cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-03-07", "url": "https://arxiv.org/abs/2303.04689", "pdf_url": "https://arxiv.org/pdf/2303.04689v4", "arxiv_id": "2303.04689", "doi": "10.1145/3634686", "citation_count": 27, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3618}
{"id": "10b8e852dddbf577edac8c6eb5eea8e6b54f9704aef86c712c9ad1f961edcca7", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Interpretable Federated Learning", "abstract": "Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data. In order for FL to achieve widespread adoption, it is important to balance the need for performance, privacy-preservation and interpretability, especially in mission critical applications such as finance and healthcare. Thus, interpretable federated learning (IFL) has become an emerging topic of research attracting significant interest from the academia and the industry alike. Its interdisciplinary nature can be challenging for new researchers to pick up. In this paper, we bridge this gap by providing (to the best of our knowledge) the first survey on IFL. We propose a unique IFL taxonomy which covers relevant works enabling FL models to explain the prediction results, support model debugging, and provide insights into the contributions made by individual data owners or data samples, which in turn, is crucial for allocating rewards fairly to motivate active and reliable participation in FL. We conduct comprehensive analysis of the representative IFL approaches, the commonly adopted performance evaluation metrics, and promising directions towards building versatile IFL techniques.", "authors": ["Anran Li", "Rui Liu", "Ming Hu", "Yuanyuan Chen", "Shipeng Wang", "Lizhen Cui", "Han Yu"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-02-27", "url": "https://arxiv.org/abs/2302.13473", "pdf_url": "https://arxiv.org/pdf/2302.13473v2", "arxiv_id": "2302.13473", "doi": "10.48550/arXiv.2302.13473", "citation_count": 20, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3306}
{"id": "e373e167399a09d57554b0ae93c1e677eedcc97824084f17eb5ec5ac446cf2bb", "sources": ["arxiv", "semantic_scholar"], "title": "Exploratory Analysis of Federated Learning Methods with Differential Privacy on MIMIC-III", "abstract": "Background: Federated learning methods offer the possibility of training machine learning models on privacy-sensitive data sets, which cannot be easily shared. Multiple regulations pose strict requirements on the storage and usage of healthcare data, leading to data being in silos (i.e. locked-in at healthcare facilities). The application of federated algorithms on these datasets could accelerate disease diagnostic, drug development, as well as improve patient care. Methods: We present an extensive evaluation of the impact of different federation and differential privacy techniques when training models on the open-source MIMIC-III dataset. We analyze a set of parameters influencing a federated model performance, namely data distribution (homogeneous and heterogeneous), communication strategies (communication rounds vs. local training epochs), federation strategies (FedAvg vs. FedProx). Furthermore, we assess and compare two differential privacy (DP) techniques during model training: a stochastic gradient descent-based differential privacy algorithm (DP-SGD), and a sparse vector differential privacy technique (DP-SVT). Results: Our experiments show that extreme data distributions across sites (imbalance either in the number of patients or the positive label ratios between sites) lead to a deterioration of model performance when trained using the FedAvg strategy. This issue is resolved when using FedProx with the use of appropriate hyperparameter tuning. Furthermore, the results show that both differential privacy techniques can reach model performances similar to those of models trained without DP, however at the expense of a large quantifiable privacy leakage. Conclusions: We evaluate empirically the benefits of two federation strategies and propose optimal strategies for the choice of parameters when using differential privacy techniques.", "authors": ["Aron N. Horvath", "Matteo Berchier", "Farhad Nooralahzadeh", "Ahmed Allam", "Michael Krauthammer"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2023-02-08", "url": "https://arxiv.org/abs/2302.04208", "pdf_url": "https://arxiv.org/pdf/2302.04208v1", "arxiv_id": "2302.04208", "doi": "10.48550/arXiv.2302.04208", "citation_count": 7, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2258}
{"id": "ff9345a062fa324428aad8030b0f26242e509d02b9d8d22b7fda601f8810733b", "sources": ["arxiv", "semantic_scholar"], "title": "FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation", "abstract": "Vertical federated learning (VFL) allows an active party with labeled feature to leverage auxiliary features from the passive parties to improve model performance. Concerns about the private feature and label leakage in both the training and inference phases of VFL have drawn wide research attention. In this paper, we propose a general privacy-preserving vertical federated deep learning framework called FedPass, which leverages adaptive obfuscation to protect the feature and label simultaneously. Strong privacy-preserving capabilities about private features and labels are theoretically proved (in Theorems 1 and 2). Extensive experimental result s with different datasets and network architectures also justify the superiority of FedPass against existing methods in light of its near-optimal trade-off between privacy and model performance.", "authors": ["Hanlin Gu", "Jiahuan Luo", "Yan Kang", "Lixin Fan", "Qiang Yang"], "categories": ["cs.DC", "cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-01-30", "url": "https://arxiv.org/abs/2301.12623", "pdf_url": "https://arxiv.org/pdf/2301.12623v2", "arxiv_id": "2301.12623", "doi": "10.48550/arXiv.2301.12623", "citation_count": 19, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "International Joint Conference on Artificial Intelligence", "quality_score": 0.3253}
{"id": "855ba9a36157d1daf8da169c149e79c1ee2bbfdd29f2ef2b17bb8ce304a1b6dd", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning", "abstract": "Proactive edge association is capable of improving wireless connectivity at the cost of increased handover (HO) frequency and energy consumption, while relying on a large amount of private information sharing required for decision making. In order to improve the connectivity-cost trade-off without privacy leakage, we investigate the privacy-preserving joint edge association and power allocation (JEAPA) problem in the face of the environmental uncertainty and the infeasibility of individual learning. Upon modelling the problem by a decentralized partially observable Markov Decision Process (Dec-POMDP), it is solved by federated multi-agent reinforcement learning (FMARL) through only sharing encrypted training data for federatively learning the policy sought. Our simulation results show that the proposed solution strikes a compelling trade-off, while preserving a higher privacy level than the state-of-the-art solutions.", "authors": ["Yan Lin", "Jinming Bao", "Yijin Zhang", "Jun Li", "Feng Shu", "Lajos Hanzo"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2023-01-26", "url": "https://arxiv.org/abs/2301.11014", "pdf_url": "https://arxiv.org/pdf/2301.11014v1", "arxiv_id": "2301.11014", "doi": "10.1109/TVT.2023.3240682", "citation_count": 13, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Vehicular Technology", "quality_score": 0.2865}
{"id": "f14b0e0808f370299f2752b8b8e346e30260b93dfa3634a6b44874f4a57d5330", "sources": ["arxiv", "semantic_scholar"], "title": "Combined Use of Federated Learning and Image Encryption for Privacy-Preserving Image Classification with Vision Transformer", "abstract": "In recent years, privacy-preserving methods for deep learning have become an urgent problem. Accordingly, we propose the combined use of federated learning (FL) and encrypted images for privacy-preserving image classification under the use of the vision transformer (ViT). The proposed method allows us not only to train models over multiple participants without directly sharing their raw data but to also protect the privacy of test (query) images for the first time. In addition, it can also maintain the same accuracy as normally trained models. In an experiment, the proposed method was demonstrated to well work without any performance degradation on the CIFAR-10 and CIFAR-100 datasets.", "authors": ["Teru Nagamori", "Hitoshi Kiya"], "categories": ["cs.CV", "cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-01-23", "url": "https://arxiv.org/abs/2301.09255", "pdf_url": "https://arxiv.org/pdf/2301.09255v2", "arxiv_id": "2301.09255", "doi": "10.48550/arXiv.2301.09255", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "b20b6b2439fcfe95919a22564fdc5566aba428f3f855c4ea3747edd6bfde4f6d", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy and Efficiency of Communications in Federated Split Learning", "abstract": "Everyday, large amounts of sensitive data is distributed across mobile phones, wearable devices, and other sensors. Traditionally, these enormous datasets have been processed on a single system, with complex models being trained to make valuable predictions. Distributed machine learning techniques such as Federated and Split Learning have recently been developed to protect user data and privacy better while ensuring high performance. Both of these distributed learning architectures have advantages and disadvantages. In this paper, we examine these tradeoffs and suggest a new hybrid Federated Split Learning architecture that combines the efficiency and privacy benefits of both. Our evaluation demonstrates how our hybrid Federated Split Learning approach can lower the amount of processing power required by each client running a distributed learning system, reduce training and inference time while keeping a similar accuracy. We also discuss the resiliency of our approach to deep learning privacy inference attacks and compare our solution to other recently proposed benchmarks.", "authors": ["Zongshun Zhang", "Andrea Pinto", "Valeria Turina", "Flavio Esposito", "Ibrahim Matta"], "categories": ["cs.LG", "cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2023-01-04", "url": "https://arxiv.org/abs/2301.01824", "pdf_url": "https://arxiv.org/pdf/2301.01824v2", "arxiv_id": "2301.01824", "doi": "10.1109/TBDATA.2023.3280405", "citation_count": 61, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Big Data", "quality_score": 0.4481}
{"id": "f17a62bed4d58afa746532c92c77c95193591d163c7d636c9885d802f67bba3f", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy Considerations for Risk-Based Authentication Systems", "abstract": "Risk-based authentication (RBA) extends authentication mechanisms to make them more robust against account takeover attacks, such as those using stolen passwords. RBA is recommended by NIST and NCSC to strengthen password-based authentication, and is already used by major online services. Also, users consider RBA to be more usable than two-factor authentication and just as secure. However, users currently obtain RBA's high security and usability benefits at the cost of exposing potentially sensitive personal data (e.g., IP address or browser information). This conflicts with user privacy and requires to consider user rights regarding the processing of personal data. We outline potential privacy challenges regarding different attacker models and propose improvements to balance privacy in RBA systems. To estimate the properties of the privacy-preserving RBA enhancements in practical environments, we evaluated a subset of them with long-term data from 780 users of a real-world online service. Our results show the potential to increase privacy in RBA solutions. However, it is limited to certain parameters that should guide RBA design to protect privacy. We outline research directions that need to be considered to achieve a widespread adoption of privacy preserving RBA with high user acceptance.", "authors": ["Stephan Wiefling", "Jan Tolsdorf", "Luigi Lo Iacono"], "categories": ["cs.CR", "cs.HC"], "fields_of_study": ["Computer Science"], "published_date": "2023-01-04", "url": "https://arxiv.org/abs/2301.01505", "pdf_url": "https://arxiv.org/pdf/2301.01505v1", "arxiv_id": "2301.01505", "doi": "10.1109/EuroSPW54576.2021.00040", "citation_count": 13, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "2021 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), pp. 320-327", "quality_score": 0.2865}
{"id": "f6166aef1114fc9df3f6896747571591d8e2e8d03af9d06b2112eb9431ca97a6", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management", "abstract": "The utilization of large-scale distributed renewable energy promotes the development of the multi-microgrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy-sufficiency. The multi-agent deep reinforcement learning (MADRL) has been widely used for the energy management problem because of its real-time scheduling ability. However, its training requires massive energy operation data of microgrids (MGs), while gathering these data from different MGs would threaten their privacy and data security. Therefore, this paper tackles this practical yet challenging issue by proposing a federated multi-agent deep reinforcement learning (F-MADRL) algorithm via the physics-informed reward. In this algorithm, the federated learning (FL) mechanism is introduced to train the F-MADRL algorithm thus ensures the privacy and the security of data. In addition, a decentralized MMG model is built, and the energy of each participated MG is managed by an agent, which aims to minimize economic costs and keep self energy-sufficiency according to the physics-informed reward. At first, MGs individually execute the self-training based on local energy operation data to train their local agent models. Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents. In this way, the experience of each MG agent can be shared and the energy operation data is not explicitly transmitted, thus protecting the privacy and ensuring data security. Finally, experiments are conducted on Oak Ridge national laboratory distributed energy control communication lab microgrid (ORNL-MG) test system, and the comparisons are carried out to verify the effectiveness of introducing the FL mechanism and the outperformance of our proposed F-MADRL.", "authors": ["Yuanzheng Li", "Shangyang He", "Yang Li", "Yang Shi", "Zhigang Zeng"], "categories": ["eess.SY", "cs.LG"], "fields_of_study": ["Computer Science", "Medicine", "Engineering"], "published_date": "2022-12-29", "url": "https://arxiv.org/abs/2301.00641", "pdf_url": "https://arxiv.org/pdf/2301.00641v1", "arxiv_id": "2301.00641", "doi": "10.1109/TNNLS.2022.3232630", "citation_count": 108, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Neural Networks and Learning Systems", "quality_score": 0.5094}
{"id": "c6e3fd69cd6b91a50d99401ab8499cadb62ea03b8da249443d224796ebcc7895", "sources": ["arxiv", "semantic_scholar"], "title": "Social-Aware Clustered Federated Learning with Customized Privacy Preservation", "abstract": "A key feature of federated learning (FL) is to preserve the data privacy of end users. However, there still exist potential privacy leakage in exchanging gradients under FL. As a result, recent research often explores the differential privacy (DP) approaches to add noises to the computing results to address privacy concerns with low overheads, which however degrade the model performance. In this paper, we strike the balance of data privacy and efficiency by utilizing the pervasive social connections between users. Specifically, we propose SCFL, a novel Social-aware Clustered Federated Learning scheme, where mutually trusted individuals can freely form a social cluster and aggregate their raw model updates (e.g., gradients) inside each cluster before uploading to the cloud for global aggregation. By mixing model updates in a social group, adversaries can only eavesdrop the social-layer combined results, but not the privacy of individuals. We unfold the design of SCFL in three steps.i) Stable social cluster formation. Considering users' heterogeneous training samples and data distributions, we formulate the optimal social cluster formation problem as a federation game and devise a fair revenue allocation mechanism to resist free-riders. ii) Differentiated trust-privacy mapping}. For the clusters with low mutual trust, we design a customizable privacy preservation mechanism to adaptively sanitize participants' model updates depending on social trust degrees. iii) Distributed convergence}. A distributed two-sided matching algorithm is devised to attain an optimized disjoint partition with Nash-stable convergence. Experiments on Facebook network and MNIST/CIFAR-10 datasets validate that our SCFL can effectively enhance learning utility, improve user payoff, and enforce customizable privacy protection.", "authors": ["Yuntao Wang", "Zhou Su", "Yanghe Pan", "Tom H Luan", "Ruidong Li", "Shui Yu"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-12-25", "url": "https://arxiv.org/abs/2212.13992", "pdf_url": "https://arxiv.org/pdf/2212.13992v3", "arxiv_id": "2212.13992", "doi": "10.1109/TNET.2024.3379439", "citation_count": 26, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "IEEE/ACM Transactions on Networking", "quality_score": 0.3578}
{"id": "ebef3ba855cd9bb6173437b65d040741fa82782fa2481b5f1d828c74cf3fc452", "sources": ["arxiv", "semantic_scholar"], "title": "Plankton-FL: Exploration of Federated Learning for Privacy-Preserving Training of Deep Neural Networks for Phytoplankton Classification", "abstract": "Creating high-performance generalizable deep neural networks for phytoplankton monitoring requires utilizing large-scale data coming from diverse global water sources. A major challenge to training such networks lies in data privacy, where data collected at different facilities are often restricted from being transferred to a centralized location. A promising approach to overcome this challenge is federated learning, where training is done at site level on local data, and only the model parameters are exchanged over the network to generate a global model. In this study, we explore the feasibility of leveraging federated learning for privacy-preserving training of deep neural networks for phytoplankton classification. More specifically, we simulate two different federated learning frameworks, federated learning (FL) and mutually exclusive FL (ME-FL), and compare their performance to a traditional centralized learning (CL) framework. Experimental results from this study demonstrate the feasibility and potential of federated learning for phytoplankton monitoring.", "authors": ["Daniel Zhang", "Vikram Voleti", "Alexander Wong", "Jason Deglint"], "categories": ["cs.LG", "cs.CR", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2022-12-18", "url": "https://arxiv.org/abs/2212.08990", "pdf_url": "https://arxiv.org/pdf/2212.08990v1", "arxiv_id": "2212.08990", "doi": "10.48550/arXiv.2212.08990", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "7e3a5431542343ee509df15f59f841f2fe6f01209d5424e4de3833390d5f58db", "sources": ["arxiv", "semantic_scholar"], "title": "Client Selection for Federated Bayesian Learning", "abstract": "Distributed Stein Variational Gradient Descent (DSVGD) is a non-parametric distributed learning framework for federated Bayesian learning, where multiple clients jointly train a machine learning model by communicating a number of non-random and interacting particles with the server. Since communication resources are limited, selecting the clients with most informative local learning updates can improve the model convergence and communication efficiency. In this paper, we propose two selection schemes for DSVGD based on Kernelized Stein Discrepancy (KSD) and Hilbert Inner Product (HIP). We derive the upper bound on the decrease of the global free energy per iteration for both schemes, which is then minimized to speed up the model convergence. We evaluate and compare our schemes with conventional schemes in terms of model accuracy, convergence speed, and stability using various learning tasks and datasets.", "authors": ["Jiarong Yang", "Yuan Liu", "Rahif Kassab"], "categories": ["cs.LG", "eess.SP"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2022-12-11", "url": "https://arxiv.org/abs/2212.05492", "pdf_url": "https://arxiv.org/pdf/2212.05492v2", "arxiv_id": "2212.05492", "doi": "10.1109/JSAC.2023.3242720", "citation_count": 18, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "IEEE Journal on Selected Areas in Communications", "quality_score": 0.3197}
{"id": "29044b1bbbfec891dab59f818adaa391b27e84b694aa0f3a3e68e116d8467e8c", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning", "abstract": "Terabytes of data are collected by wind turbine manufacturers from their fleets every day. And yet, a lack of data access and sharing impedes exploiting the full potential of the data. We present a distributed machine learning approach that preserves the data privacy by leaving the data on the wind turbines while still enabling fleet-wide learning on those local data. We show that through federated fleet-wide learning, turbines with little or no representative training data can benefit from more accurate normal behavior models. Customizing the global federated model to individual turbines yields the highest fault detection accuracy in cases where the monitored target variable is distributed heterogeneously across the fleet. We demonstrate this for bearing temperatures, a target variable whose normal behavior can vary widely depending on the turbine. We show that no turbine experiences a loss in model performance from participating in the federated learning process, resulting in superior performance of the federated learning strategy in our case studies. The distributed learning increases the normal behavior model training times by about a factor of ten due to increased communication overhead and slower model convergence.", "authors": ["Lorin Jenkel", "Stefan Jonas", "Angela Meyer"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2022-12-07", "url": "https://arxiv.org/abs/2212.03529", "pdf_url": "https://arxiv.org/pdf/2212.03529v3", "arxiv_id": "2212.03529", "doi": "10.3390/en16176377", "citation_count": 15, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Energies", "quality_score": 0.301}
{"id": "f02789cddce5f8f582c343143d8f6163221d1e788f3d65eae7759a8c37809835", "sources": ["arxiv", "semantic_scholar"], "title": "IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images with Deep Learning", "abstract": "Smart sensors, devices and systems deployed in smart cities have brought improved physical protections to their citizens. Enhanced crime prevention, and fire and life safety protection are achieved through these technologies that perform motion detection, threat and actors profiling, and real-time alerts. However, an important requirement in these increasingly prevalent deployments is the preservation of privacy and enforcement of protection of personal identifiable information. Thus, strong encryption and anonymization techniques should be applied to the collected data. In this IEEE Big Data Cup 2022 challenge, different masking, encoding and homomorphic encryption techniques were applied to the images to protect the privacy of their contents. Participants are required to develop detection solutions to perform privacy preserving matching of these images. In this paper, we describe our solution which is based on state-of-the-art deep convolutional neural networks and various data augmentation techniques. Our solution achieved 1st place at the IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images Challenge.", "authors": ["Vrizlynn L. L. Thing"], "categories": ["cs.CR", "cs.AI", "cs.CV", "cs.LG", "cs.MM"], "fields_of_study": ["Computer Science"], "published_date": "2022-11-18", "url": "https://arxiv.org/abs/2211.11565", "pdf_url": "https://arxiv.org/pdf/2211.11565v1", "arxiv_id": "2211.11565", "doi": "10.1109/BigData55660.2022.10020250", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Big Data, IEEE BigData, 2022", "quality_score": 0.1193}
{"id": "14204ec6f1dad1c23b63a8b153e9b543d4ded20e8d2f88cac685cae3f57ba311", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Privacy-Aware Causal Structure Learning in Federated Setting", "abstract": "Causal structure learning has been extensively studied and widely used in machine learning and various applications. To achieve an ideal performance, existing causal structure learning algorithms often need to centralize a large amount of data from multiple data sources. However, in the privacy-preserving setting, it is impossible to centralize data from all sources and put them together as a single dataset. To preserve data privacy, federated learning as a new learning paradigm has attracted much attention in machine learning in recent years. In this paper, we study a privacy-aware causal structure learning problem in the federated setting and propose a novel Federated PC (FedPC) algorithm with two new strategies for preserving data privacy without centralizing data. Specifically, we first propose a novel layer-wise aggregation strategy for a seamless adaptation of the PC algorithm into the federated learning paradigm for federated skeleton learning, then we design an effective strategy for learning consistent separation sets for federated edge orientation. The extensive experiments validate that FedPC is effective for causal structure learning in a federated learning setting.", "authors": ["Jianli Huang", "Xianjie Guo", "Kui Yu", "Fuyuan Cao", "Jiye Liang"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2022-11-13", "url": "https://arxiv.org/abs/2211.06919", "pdf_url": "https://arxiv.org/pdf/2211.06919v2", "arxiv_id": "2211.06919", "doi": "10.1109/TBDATA.2023.3285477", "citation_count": 20, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Big Data", "quality_score": 0.3306}
{"id": "9f8bfcebb9824d145298bc1e110237cd8398fa8aad4599da6aa54f1ddb6a5227", "sources": ["arxiv", "semantic_scholar"], "title": "FL Games: A Federated Learning Framework for Distribution Shifts", "abstract": "Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. However, participating clients typically each hold data from a different distribution, which can yield to catastrophic generalization on data from a different client, which represents a new domain. In this work, we argue that in order to generalize better across non-i.i.d. clients, it is imperative to only learn correlations that are stable and invariant across domains. We propose FL GAMES, a game-theoretic framework for federated learning that learns causal features that are invariant across clients. While training to achieve the Nash equilibrium, the traditional best response strategy suffers from high-frequency oscillations. We demonstrate that FL GAMES effectively resolves this challenge and exhibits smooth performance curves. Further, FL GAMES scales well in the number of clients, requires significantly fewer communication rounds, and is agnostic to device heterogeneity. Through empirical evaluation, we demonstrate that FL GAMES achieves high out-of-distribution performance on various benchmarks.", "authors": ["Sharut Gupta", "Kartik Ahuja", "Mohammad Havaei", "Niladri Chatterjee", "Yoshua Bengio"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-10-31", "url": "https://arxiv.org/abs/2211.00184", "pdf_url": "https://arxiv.org/pdf/2211.00184v1", "arxiv_id": "2211.00184", "doi": "10.48550/arXiv.2205.11101", "citation_count": 24, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3495}
{"id": "8f6b436b6f6dda4116650051df0720f31a80f104e74b94f34123d2399b82eeea", "sources": ["arxiv", "semantic_scholar"], "title": "NVIDIA FLARE: Federated Learning from Simulation to Real-World", "abstract": "Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data. We created NVIDIA FLARE as an open-source software development kit (SDK) to make it easier for data scientists to use FL in their research and real-world applications. The SDK includes solutions for state-of-the-art FL algorithms and federated machine learning approaches, which facilitate building workflows for distributed learning across enterprises and enable platform developers to create a secure, privacy-preserving offering for multiparty collaboration utilizing homomorphic encryption or differential privacy. The SDK is a lightweight, flexible, and scalable Python package. It allows researchers to apply their data science workflows in any training libraries (PyTorch, TensorFlow, XGBoost, or even NumPy) in real-world FL settings. This paper introduces the key design principles of NVFlare and illustrates some use cases (e.g., COVID analysis) with customizable FL workflows that implement different privacy-preserving algorithms. Code is available at https://github.com/NVIDIA/NVFlare.", "authors": ["Holger R. Roth", "Yan Cheng", "Yuhong Wen", "Isaac Yang", "Ziyue Xu", "Yuan-Ting Hsieh", "Kristopher Kersten", "Ahmed Harouni", "Can Zhao", "Kevin Lu", "Zhihong Zhang", "Wenqi Li", "Andriy Myronenko", "Dong Yang", "Sean Yang", "Nicola Rieke", "Abood Quraini", "Chester Chen", "Daguang Xu", "Nic Ma", "Prerna Dogra", "Mona Flores", "Andrew Feng"], "categories": ["cs.LG", "cs.AI", "cs.CV", "cs.NI", "cs.SE"], "fields_of_study": ["Computer Science"], "published_date": "2022-10-24", "url": "https://arxiv.org/abs/2210.13291", "pdf_url": "https://arxiv.org/pdf/2210.13291v3", "arxiv_id": "2210.13291", "doi": "10.48550/arXiv.2210.13291", "citation_count": 174, "influential_citation_count": 7, "has_code": true, "code_url": "https://github.com/NVIDIA/NVFlare", "venue": "IEEE Data Engineering Bulletin", "quality_score": 0.5608}
{"id": "1da12d739370d00d3740a9700b570028252684fa618c8db8244f3c1f6359b888", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning with Privacy-Preserving Ensemble Attention Distillation", "abstract": "Federated Learning (FL) is a machine learning paradigm where many local nodes collaboratively train a central model while keeping the training data decentralized. This is particularly relevant for clinical applications since patient data are usually not allowed to be transferred out of medical facilities, leading to the need for FL. Existing FL methods typically share model parameters or employ co-distillation to address the issue of unbalanced data distribution. However, they also require numerous rounds of synchronized communication and, more importantly, suffer from a privacy leakage risk. We propose a privacy-preserving FL framework leveraging unlabeled public data for one-way offline knowledge distillation in this work. The central model is learned from local knowledge via ensemble attention distillation. Our technique uses decentralized and heterogeneous local data like existing FL approaches, but more importantly, it significantly reduces the risk of privacy leakage. We demonstrate that our method achieves very competitive performance with more robust privacy preservation based on extensive experiments on image classification, segmentation, and reconstruction tasks.", "authors": ["Xuan Gong", "Liangchen Song", "Rishi Vedula", "Abhishek Sharma", "Meng Zheng", "Benjamin Planche", "Arun Innanje", "Terrence Chen", "Junsong Yuan", "David Doermann", "Ziyan Wu"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2022-10-16", "url": "https://arxiv.org/abs/2210.08464", "pdf_url": "https://arxiv.org/pdf/2210.08464v1", "arxiv_id": "2210.08464", "doi": "10.1109/TMI.2022.3213244", "citation_count": 45, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Medical Imaging", "quality_score": 0.4157}
{"id": "d360bbc7cf72419c808ad45b34a162ce22e103fca94aad7ad55aebcc89838edd", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph", "abstract": "Establishing how a set of learners can provide privacy-preserving federated learning in a fully decentralized (peer-to-peer, no coordinator) manner is an open problem. We propose the first privacy-preserving consensus-based algorithm for the distributed learners to achieve decentralized global model aggregation in an environment of high mobility, where the communication graph between the learners may vary between successive rounds of model aggregation. In particular, in each round of global model aggregation, the Metropolis-Hastings method is applied to update the weighted adjacency matrix based on the current communication topology. In addition, the Shamir's secret sharing scheme is integrated to facilitate privacy in reaching consensus of the global model. The paper establishes the correctness and privacy properties of the proposed algorithm. The computational efficiency is evaluated by a simulation built on a federated learning framework with a real-word dataset.", "authors": ["Yang Lu", "Zhengxin Yu", "Neeraj Suri"], "categories": ["cs.CR", "cs.LG", "cs.MA"], "fields_of_study": ["Computer Science"], "published_date": "2022-10-01", "url": "https://arxiv.org/abs/2210.00325", "pdf_url": "https://arxiv.org/pdf/2210.00325v1", "arxiv_id": "2210.00325", "doi": "10.1145/3591354", "citation_count": 31, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "ACM Transactions on Privacy and Security", "quality_score": 0.3763}
{"id": "bc087fe55f82a42c1261b6d3b878da63eaf2f84392a46d3cb31fd1e0cdcb9720", "sources": ["arxiv", "semantic_scholar"], "title": "Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning", "abstract": "Normalization is an important but understudied challenge in privacy-related application domains such as federated learning (FL), differential privacy (DP), and differentially private federated learning (DP-FL). While the unsuitability of batch normalization for these domains has already been shown, the impact of other normalization methods on the performance of federated or differentially private models is not well-known. To address this, we draw a performance comparison among layer normalization (LayerNorm), group normalization (GroupNorm), and the recently proposed kernel normalization (KernelNorm) in FL, DP, and DP-FL settings. Our results indicate LayerNorm and GroupNorm provide no performance gain compared to the baseline (i.e. no normalization) for shallow models in FL and DP. They, on the other hand, considerably enhance the performance of shallow models in DP-FL and deeper models in FL and DP. KernelNorm, moreover, significantly outperforms its competitors in terms of accuracy and convergence rate (or communication efficiency) for both shallow and deeper models in all considered learning environments. Given these key observations, we propose a kernel normalized ResNet architecture called KNResNet-13 for differentially private learning. Using the proposed architecture, we provide new state-of-the-art accuracy values on the CIFAR-10 and Imagenette datasets, when trained from scratch.", "authors": ["Reza Nasirigerdeh", "Javad Torkzadehmahani", "Daniel Rueckert", "Georgios Kaissis"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2022-09-30", "url": "https://arxiv.org/abs/2210.00053", "pdf_url": "https://arxiv.org/pdf/2210.00053v2", "arxiv_id": "2210.00053", "doi": "10.1109/SaTML54575.2023.00016", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "1st IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2023", "quality_score": 0.0753}
{"id": "2e8daf396e5da7b3e0a5a9e78c75356b73c1f37e10eefc33520ab8c8a84ee3e9", "sources": ["arxiv", "semantic_scholar"], "title": "Momentum Gradient Descent Federated Learning with Local Differential Privacy", "abstract": "Nowadays, the development of information technology is growing rapidly. In the big data era, the privacy of personal information has been more pronounced. The major challenge is to find a way to guarantee that sensitive personal information is not disclosed while data is published and analyzed. Centralized differential privacy is established on the assumption of a trusted third-party data curator. However, this assumption is not always true in reality. As a new privacy preservation model, local differential privacy has relatively strong privacy guarantees. Although federated learning has relatively been a privacy-preserving approach for distributed learning, it still introduces various privacy concerns. To avoid privacy threats and reduce communication costs, in this article, we propose integrating federated learning and local differential privacy with momentum gradient descent to improve the performance of machine learning models.", "authors": ["Mengde Han", "Tianqing Zhu", "Wanlei Zhou"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-09-28", "url": "https://arxiv.org/abs/2209.14086", "pdf_url": "https://arxiv.org/pdf/2209.14086v2", "arxiv_id": "2209.14086", "doi": "10.48550/arXiv.2209.14086", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "5be5ac3fda88c8f090ee96c24c99071333ff1bbdafa095e2f67a8f513bad4b17", "sources": ["arxiv", "semantic_scholar"], "title": "FedToken: Tokenized Incentives for Data Contribution in Federated Learning", "abstract": "Incentives that compensate for the involved costs in the decentralized training of a Federated Learning (FL) model act as a key stimulus for clients' long-term participation. However, it is challenging to convince clients for quality participation in FL due to the absence of: (i) full information on the client's data quality and properties; (ii) the value of client's data contributions; and (iii) the trusted mechanism for monetary incentive offers. This often leads to poor efficiency in training and communication. While several works focus on strategic incentive designs and client selection to overcome this problem, there is a major knowledge gap in terms of an overall design tailored to the foreseen digital economy, including Web 3.0, while simultaneously meeting the learning objectives. To address this gap, we propose a contribution-based tokenized incentive scheme, namely \\texttt{FedToken}, backed by blockchain technology that ensures fair allocation of tokens amongst the clients that corresponds to the valuation of their data during model training. Leveraging the engineered Shapley-based scheme, we first approximate the contribution of local models during model aggregation, then strategically schedule clients lowering the communication rounds for convergence and anchor ways to allocate \\emph{affordable} tokens under a constrained monetary budget. Extensive simulations demonstrate the efficacy of our proposed method.", "authors": ["Shashi Raj Pandey", "Lam Duc Nguyen", "Petar Popovski"], "categories": ["cs.LG", "cs.DC", "cs.GT", "cs.NI"], "fields_of_study": ["Computer Science"], "published_date": "2022-09-20", "url": "https://arxiv.org/abs/2209.09775", "pdf_url": "https://arxiv.org/pdf/2209.09775v2", "arxiv_id": "2209.09775", "doi": "10.48550/arXiv.2209.09775", "citation_count": 17, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3138}
{"id": "cef242fb173e62cd6dc9e96ca712065cc7086f6f00b411d9dea0d7394b9e3735", "sources": ["arxiv", "semantic_scholar"], "title": "Concealing Sensitive Samples against Gradient Leakage in Federated Learning", "abstract": "Federated Learning (FL) is a distributed learning paradigm that enhances users privacy by eliminating the need for clients to share raw, private data with the server. Despite the success, recent studies expose the vulnerability of FL to model inversion attacks, where adversaries reconstruct users private data via eavesdropping on the shared gradient information. We hypothesize that a key factor in the success of such attacks is the low entanglement among gradients per data within the batch during stochastic optimization. This creates a vulnerability that an adversary can exploit to reconstruct the sensitive data. Building upon this insight, we present a simple, yet effective defense strategy that obfuscates the gradients of the sensitive data with concealed samples. To achieve this, we propose synthesizing concealed samples to mimic the sensitive data at the gradient level while ensuring their visual dissimilarity from the actual sensitive data. Compared to the previous art, our empirical evaluations suggest that the proposed technique provides the strongest protection while simultaneously maintaining the FL performance.", "authors": ["Jing Wu", "Munawar Hayat", "Mingyi Zhou", "Mehrtash Harandi"], "categories": ["cs.LG", "cs.CR", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2022-09-13", "url": "https://arxiv.org/abs/2209.05724", "pdf_url": "https://arxiv.org/pdf/2209.05724v2", "arxiv_id": "2209.05724", "doi": "10.1609/aaai.v38i19.30171", "citation_count": 18, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "AAAI Conference on Artificial Intelligence", "quality_score": 0.3197}
{"id": "bbc66501a1d1acb815468483417cd88c2c5a8274a404540168a98a38123838f0", "sources": ["arxiv", "semantic_scholar"], "title": "Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learning", "abstract": "Federated learning has attracted growing interest as it preserves the clients' privacy. As a variant of federated learning, federated transfer learning utilizes the knowledge from similar tasks and thus has also been intensively studied. However, due to the limited radio spectrum, the communication efficiency of federated learning via wireless links is critical since some tasks may require thousands of Terabytes of uplink payload. In order to improve the communication efficiency, we in this paper propose the feature-based federated transfer learning as an innovative approach to reduce the uplink payload by more than five orders of magnitude compared to that of existing approaches. We first introduce the system design in which the extracted features and outputs are uploaded instead of parameter updates, and then determine the required payload with this approach and provide comparisons with the existing approaches. Subsequently, we analyze the random shuffling scheme that preserves the clients' privacy. Finally, we evaluate the performance of the proposed learning scheme via experiments on an image classification task to show its effectiveness.", "authors": ["Feng Wang", "M. Cenk Gursoy", "Senem Velipasalar"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-09-12", "url": "https://arxiv.org/abs/2209.05395", "pdf_url": "https://arxiv.org/pdf/2209.05395v1", "arxiv_id": "2209.05395", "doi": "10.1109/GLOBECOM48099.2022.10000612", "citation_count": 5, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/wfwf10/Feature-based-Federated-Transfer-Learning", "venue": "Global Communications Conference", "quality_score": 0.1945}
{"id": "770a84b733e87ad042bbd9250ab4feac9b4c7bf864925ff467378df62d71617c", "sources": ["arxiv", "semantic_scholar"], "title": "Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling", "abstract": "Federated Learning (FL) wherein multiple institutions collaboratively train a machine learning model without sharing data is becoming popular. Participating institutions might not contribute equally, some contribute more data, some better quality data or some more diverse data. To fairly rank the contribution of different institutions, Shapley value (SV) has emerged as the method of choice. Exact SV computation is impossibly expensive, especially when there are hundreds of contributors. Existing SV computation techniques use approximations. However, in healthcare where the number of contributing institutions are likely not of a colossal scale, computing exact SVs is still exorbitantly expensive, but not impossible. For such settings, we propose an efficient SV computation technique called SaFE (Shapley Value for Federated Learning using Ensembling). We empirically show that SaFE computes values that are close to exact SVs, and that it performs better than current SV approximations. This is particularly relevant in medical imaging setting where widespread heterogeneity across institutions is rampant and fast accurate data valuation is required to determine the contribution of each participant in multi-institutional collaborative learning.", "authors": ["Sourav Kumar", "A. Lakshminarayanan", "Ken Chang", "Feri Guretno", "Ivan Ho Mien", "Jayashree Kalpathy-Cramer", "Pavitra Krishnaswamy", "Praveer Singh"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Medicine", "Computer Science"], "published_date": "2022-09-12", "url": "https://arxiv.org/abs/2209.05424", "pdf_url": "https://arxiv.org/pdf/2209.05424v1", "arxiv_id": "2209.05424", "doi": "10.48550/arXiv.2209.05424", "citation_count": 10, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2603}
{"id": "95bee04a3de69583e194648db2b41824f2fc35fec3f96d6b7d3efb282d1454a3", "sources": ["arxiv", "semantic_scholar"], "title": "Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation", "abstract": "Federated Learning (FL) is a machine learning paradigm where local nodes collaboratively train a central model while the training data remains decentralized. Existing FL methods typically share model parameters or employ co-distillation to address the issue of unbalanced data distribution. However, they suffer from communication bottlenecks. More importantly, they risk privacy leakage. In this work, we develop a privacy preserving and communication efficient method in a FL framework with one-shot offline knowledge distillation using unlabeled, cross-domain public data. We propose a quantized and noisy ensemble of local predictions from completely trained local models for stronger privacy guarantees without sacrificing accuracy. Based on extensive experiments on image classification and text classification tasks, we show that our privacy-preserving method outperforms baseline FL algorithms with superior performance in both accuracy and communication efficiency.", "authors": ["Xuan Gong", "Abhishek Sharma", "Srikrishna Karanam", "Ziyan Wu", "Terrence Chen", "David Doermann", "Arun Innanje"], "categories": ["cs.CR", "cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-09-10", "url": "https://arxiv.org/abs/2209.04599", "pdf_url": "https://arxiv.org/pdf/2209.04599v1", "arxiv_id": "2209.04599", "doi": "10.1609/aaai.v36i11.21446", "citation_count": 116, "influential_citation_count": 12, "has_code": false, "code_url": null, "venue": "AAAI Conference on Artificial Intelligence", "quality_score": 0.557}
{"id": "7bf790373d09d155130e9c2826f96208adabcfb644b1080af94e0e009ac19ee9", "sources": ["arxiv", "semantic_scholar"], "title": "Trading Off Privacy, Utility and Efficiency in Federated Learning", "abstract": "Federated learning (FL) enables participating parties to collaboratively build a global model with boosted utility without disclosing private data information. Appropriate protection mechanisms have to be adopted to fulfill the opposing requirements in preserving \\textit{privacy} and maintaining high model \\textit{utility}. In addition, it is a mandate for a federated learning system to achieve high \\textit{efficiency} in order to enable large-scale model training and deployment. We propose a unified federated learning framework that reconciles horizontal and vertical federated learning. Based on this framework, we formulate and quantify the trade-offs between privacy leakage, utility loss, and efficiency reduction, which leads us to the No-Free-Lunch (NFL) theorem for the federated learning system. NFL indicates that it is unrealistic to expect an FL algorithm to simultaneously provide excellent privacy, utility, and efficiency in certain scenarios. We then analyze the lower bounds for the privacy leakage, utility loss and efficiency reduction for several widely-adopted protection mechanisms including \\textit{Randomization}, \\textit{Homomorphic Encryption}, \\textit{Secret Sharing} and \\textit{Compression}. Our analysis could serve as a guide for selecting protection parameters to meet particular requirements.", "authors": ["Xiaojin Zhang", "Yan Kang", "Kai Chen", "Lixin Fan", "Qiang Yang"], "categories": ["cs.LG", "cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2022-09-01", "url": "https://arxiv.org/abs/2209.00230", "pdf_url": "https://arxiv.org/pdf/2209.00230v3", "arxiv_id": "2209.00230", "doi": "10.1145/3595185", "citation_count": 80, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "ACM Transactions on Intelligent Systems and Technology", "quality_score": 0.4771}
{"id": "7e6f7debe78d6b09fdd4475544629178ea7469ec169ab4b0a17bf01dbfaf1d01", "sources": ["arxiv", "semantic_scholar"], "title": "FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs", "abstract": "As special information carriers containing both structure and feature information, graphs are widely used in graph mining, e.g., Graph Neural Networks (GNNs). However, in some practical scenarios, graph data are stored separately in multiple distributed parties, which may not be directly shared due to conflicts of interest. Hence, federated graph neural networks are proposed to address such data silo problems while preserving the privacy of each party (or client). Nevertheless, different graph data distributions among various parties, which is known as the statistical heterogeneity, may degrade the performance of naive federated learning algorithms like FedAvg. In this paper, we propose FedEgo, a federated graph learning framework based on ego-graphs to tackle the challenges above, where each client will train their local models while also contributing to the training of a global model. FedEgo applies GraphSAGE over ego-graphs to make full use of the structure information and utilizes Mixup for privacy concerns. To deal with the statistical heterogeneity, we integrate personalization into learning and propose an adaptive mixing coefficient strategy that enables clients to achieve their optimal personalization. Extensive experimental results and in-depth analysis demonstrate the effectiveness of FedEgo.", "authors": ["Taolin Zhang", "Chuan Chen", "Yaomin Chang", "Lin Shu", "Zibin Zheng"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2022-08-29", "url": "https://arxiv.org/abs/2208.13685", "pdf_url": "https://arxiv.org/pdf/2208.13685v2", "arxiv_id": "2208.13685", "doi": "10.1145/3624017", "citation_count": 34, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "ACM Transactions on Knowledge Discovery from Data", "quality_score": 0.386}
{"id": "afa8bc1a63c4b65c14876234994dc9f04407557374f8724c33198dfd2c99589c", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Sparsified Federated Neuroimaging Models via Weight Pruning", "abstract": "Federated training of large deep neural networks can often be restrictive due to the increasing costs of communicating the updates with increasing model sizes. Various model pruning techniques have been designed in centralized settings to reduce inference times. Combining centralized pruning techniques with federated training seems intuitive for reducing communication costs -- by pruning the model parameters right before the communication step. Moreover, such a progressive model pruning approach during training can also reduce training times/costs. To this end, we propose FedSparsify, which performs model pruning during federated training. In our experiments in centralized and federated settings on the brain age prediction task (estimating a person's age from their brain MRI), we demonstrate that models can be pruned up to 95% sparsity without affecting performance even in challenging federated learning environments with highly heterogeneous data distributions. One surprising benefit of model pruning is improved model privacy. We demonstrate that models with high sparsity are less susceptible to membership inference attacks, a type of privacy attack.", "authors": ["Dimitris Stripelis", "Umang Gupta", "Nikhil Dhinagar", "Greg Ver Steeg", "Paul Thompson", "José Luis Ambite"], "categories": ["cs.LG", "cs.CR", "eess.IV", "q-bio.QM"], "fields_of_study": ["Computer Science", "Engineering", "Biology"], "published_date": "2022-08-24", "url": "https://arxiv.org/abs/2208.11669", "pdf_url": "https://arxiv.org/pdf/2208.11669v1", "arxiv_id": "2208.11669", "doi": "10.48550/arXiv.2208.11669", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1747}
{"id": "f32bd8187db69f801a8ef97e0c7891bb741ade1285ce774cd54b5db53e6c2faf", "sources": ["arxiv", "semantic_scholar"], "title": "Joint Privacy Enhancement and Quantization in Federated Learning", "abstract": "Federated learning (FL) is an emerging paradigm for training machine learning models using possibly private data available at edge devices. The distributed operation of FL gives rise to challenges that are not encountered in centralized machine learning, including the need to preserve the privacy of the local datasets, and the communication load due to the repeated exchange of updated models. These challenges are often tackled individually via techniques that induce some distortion on the updated models, e.g., local differential privacy (LDP) mechanisms and lossy compression. In this work we propose a method coined joint privacy enhancement and quantization (JoPEQ), which jointly implements lossy compression and privacy enhancement in FL settings. In particular, JoPEQ utilizes vector quantization based on random lattice, a universal compression technique whose byproduct distortion is statistically equivalent to additive noise. This distortion is leveraged to enhance privacy by augmenting the model updates with dedicated multivariate privacy preserving noise. We show that JoPEQ simultaneously quantizes data according to a required bit-rate while holding a desired privacy level, without notably affecting the utility of the learned model. This is shown via analytical LDP guarantees, distortion and convergence bounds derivation, and numerical studies. Finally, we empirically assert that JoPEQ demolishes common attacks known to exploit privacy leakage.", "authors": ["Natalie Lang", "Elad Sofer", "Tomer Shaked", "Nir Shlezinger"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2022-08-23", "url": "https://arxiv.org/abs/2208.10888", "pdf_url": "https://arxiv.org/pdf/2208.10888v1", "arxiv_id": "2208.10888", "doi": "10.1109/TSP.2023.3244092", "citation_count": 78, "influential_citation_count": 9, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Signal Processing", "quality_score": 0.5}
{"id": "572cdae32aa9624aca9e8247eaa9521a56610d7164da68c530bfb160a86ba80e", "sources": ["arxiv", "semantic_scholar"], "title": "FedOS: using open-set learning to stabilize training in federated learning", "abstract": "Federated Learning is a recent approach to train statistical models on distributed datasets without violating privacy constraints. The data locality principle is preserved by sharing the model instead of the data between clients and the server. This brings many advantages but also poses new challenges. In this report, we explore this new research area and perform several experiments to deepen our understanding of what these challenges are and how different problem settings affect the performance of the final model. Finally, we present a novel approach to one of these challenges and compare it to other methods found in literature.", "authors": ["Mohamad Mohamad", "Julian Neubert", "Juan Segundo Argayo"], "categories": ["stat.ML", "cs.LG"], "fields_of_study": ["Mathematics", "Computer Science"], "published_date": "2022-08-22", "url": "https://arxiv.org/abs/2208.11512", "pdf_url": "https://arxiv.org/pdf/2208.11512v2", "arxiv_id": "2208.11512", "doi": "10.48550/arXiv.2208.11512", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "8128bd8c5764c8108d844ac1e85833c3f6fdd2d9447bdbb635a535efce9910a1", "sources": ["arxiv", "semantic_scholar"], "title": "Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images", "abstract": "Federated learning (FL) is a decentralized method enabling hospitals to collaboratively learn a model without sharing private patient data for training. In FL, participant hospitals periodically exchange training results rather than training samples with a central server. However, having access to model parameters or gradients can expose private training data samples. To address this challenge, we adopt secure multiparty computation (SMC) to establish a privacy-preserving federated learning framework. In our proposed method, the hospitals are divided into clusters. After local training, each hospital splits its model weights among other hospitals in the same cluster such that no single hospital can retrieve other hospitals' weights on its own. Then, all hospitals sum up the received weights, sending the results to the central server. Finally, the central server aggregates the results, retrieving the average of models' weights and updating the model without having access to individual hospitals' weights. We conduct experiments on a publicly available repository, The Cancer Genome Atlas (TCGA). We compare the performance of the proposed framework with differential privacy and federated averaging as the baseline. The results reveal that compared to differential privacy, our framework can achieve higher accuracy with no privacy leakage risk at a cost of higher communication overhead.", "authors": ["S. Maryam Hosseini", "Milad Sikaroudi", "Morteza Babaei", "H. R. Tizhoosh"], "categories": ["cs.CR", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-08-21", "url": "https://arxiv.org/abs/2208.10919", "pdf_url": "https://arxiv.org/pdf/2208.10919v1", "arxiv_id": "2208.10919", "doi": "10.48550/arXiv.2208.10919", "citation_count": 25, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3537}
{"id": "a67d7164012c1eeaf7f382e6392b75b94995343bb4c13e67ddc9d67c4af9071c", "sources": ["arxiv", "semantic_scholar"], "title": "Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy", "abstract": "Secure multi-party computation-based machine learning, referred to as MPL, has become an important technology to utilize data from multiple parties with privacy preservation. While MPL provides rigorous security guarantees for the computation process, the models trained by MPL are still vulnerable to attacks that solely depend on access to the models. Differential privacy could help to defend against such attacks. However, the accuracy loss brought by differential privacy and the huge communication overhead of secure multi-party computation protocols make it highly challenging to balance the 3-way trade-off between privacy, efficiency, and accuracy. In this paper, we are motivated to resolve the above issue by proposing a solution, referred to as PEA (Private, Efficient, Accurate), which consists of a secure DPSGD protocol and two optimization methods. First, we propose a secure DPSGD protocol to enforce DPSGD in secret sharing-based MPL frameworks. Second, to reduce the accuracy loss led by differential privacy noise and the huge communication overhead of MPL, we propose two optimization methods for the training process of MPL: (1) the data-independent feature extraction method, which aims to simplify the trained model structure; (2) the local data-based global model initialization method, which aims to speed up the convergence of the model training. We implement PEA in two open-source MPL frameworks: TF-Encrypted and Queqiao. The experimental results on various datasets demonstrate the efficiency and effectiveness of PEA. E.g. when $ε$ = 2, we can train a differentially private classification model with an accuracy of 88% for CIFAR-10 within 7 minutes under the LAN setting. This result significantly outperforms the one from CryptGPU, one SOTA MPL framework: it costs more than 16 hours to train a non-private deep neural network model on CIFAR-10 with the same accuracy.", "authors": ["Wenqiang Ruan", "Mingxin Xu", "Wenjing Fang", "Li Wang", "Lei Wang", "Weili Han"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-08-18", "url": "https://arxiv.org/abs/2208.08662", "pdf_url": "https://arxiv.org/pdf/2208.08662v1", "arxiv_id": "2208.08662", "doi": "10.1109/SP46215.2023.10179422", "citation_count": 25, "influential_citation_count": 5, "has_code": true, "code_url": null, "venue": "IEEE Symposium on Security and Privacy", "quality_score": 0.3891}
{"id": "44bade6be91f71b0a0c404de6c3c5e02359c4bec47595199d9ccf45c482319ea", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Quantum Natural Gradient Descent for Quantum Federated Learning", "abstract": "The heart of Quantum Federated Learning (QFL) is associated with a distributed learning architecture across several local quantum devices and a more efficient training algorithm for the QFL is expected to minimize the communication overhead among different quantum participants. In this work, we put forth an efficient learning algorithm, namely federated quantum natural gradient descent (FQNGD), applied in a QFL framework which consists of the variational quantum circuit (VQC)-based quantum neural networks (QNN). The FQNGD algorithm admits much fewer training iterations for the QFL model to get converged and it can significantly reduce the total communication cost among local quantum devices. Compared with other federated learning algorithms, our experiments on a handwritten digit classification dataset corroborate the effectiveness of the FQNGD algorithm for the QFL in terms of a faster convergence rate on the training dataset and higher accuracy on the test one.", "authors": ["Jun Qi"], "categories": ["quant-ph", "cs.DC", "cs.LG"], "fields_of_study": ["Computer Science", "Physics"], "published_date": "2022-08-15", "url": "https://arxiv.org/abs/2209.00564", "pdf_url": "https://arxiv.org/pdf/2209.00564v1", "arxiv_id": "2209.00564", "doi": "10.48550/arXiv.2209.00564", "citation_count": 27, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3618}
{"id": "743a7cfdeeb19d2bc1de3d19735efefba466aee0ea9636e2dd341a4fad363907", "sources": ["arxiv", "semantic_scholar"], "title": "How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?", "abstract": "Federated learning (FL) has attracted growing interest for enabling privacy-preserving machine learning on data stored at multiple users while avoiding moving the data off-device. However, while data never leaves users' devices, privacy still cannot be guaranteed since significant computations on users' training data are shared in the form of trained local models. These local models have recently been shown to pose a substantial privacy threat through different privacy attacks such as model inversion attacks. As a remedy, Secure Aggregation (SA) has been developed as a framework to preserve privacy in FL, by guaranteeing the server can only learn the global aggregated model update but not the individual model updates. While SA ensures no additional information is leaked about the individual model update beyond the aggregated model update, there are no formal guarantees on how much privacy FL with SA can actually offer; as information about the individual dataset can still potentially leak through the aggregated model computed at the server. In this work, we perform a first analysis of the formal privacy guarantees for FL with SA. Specifically, we use Mutual Information (MI) as a quantification metric and derive upper bounds on how much information about each user's dataset can leak through the aggregated model update. When using the FedSGD aggregation algorithm, our theoretical bounds show that the amount of privacy leakage reduces linearly with the number of users participating in FL with SA. To validate our theoretical bounds, we use an MI Neural Estimator to empirically evaluate the privacy leakage under different FL setups on both the MNIST and CIFAR10 datasets. Our experiments verify our theoretical bounds for FedSGD, which show a reduction in privacy leakage as the number of users and local batch size grow, and an increase in privacy leakage with the number of training rounds.", "authors": ["Ahmed Roushdy Elkordy", "Jiang Zhang", "Yahya H. Ezzeldin", "Konstantinos Psounis", "Salman Avestimehr"], "categories": ["cs.LG", "cs.CR", "cs.IT"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2022-08-03", "url": "https://arxiv.org/abs/2208.02304", "pdf_url": "https://arxiv.org/pdf/2208.02304v1", "arxiv_id": "2208.02304", "doi": "10.48550/arXiv.2208.02304", "citation_count": 49, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "Proceedings on Privacy Enhancing Technologies", "quality_score": 0.4247}
{"id": "129399054d87c73232b6763229fdd8f7b9fc4e0cb7e77ab5402b452f056fef30", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Federated Recurrent Neural Networks", "abstract": "We present RHODE, a novel system that enables privacy-preserving training of and prediction on Recurrent Neural Networks (RNNs) in a cross-silo federated learning setting by relying on multiparty homomorphic encryption. RHODE preserves the confidentiality of the training data, the model, and the prediction data; and it mitigates federated learning attacks that target the gradients under a passive-adversary threat model. We propose a packing scheme, multi-dimensional packing, for a better utilization of Single Instruction, Multiple Data (SIMD) operations under encryption. With multi-dimensional packing, RHODE enables the efficient processing, in parallel, of a batch of samples. To avoid the exploding gradients problem, RHODE provides several clipping approximations for performing gradient clipping under encryption. We experimentally show that the model performance with RHODE remains similar to non-secure solutions both for homogeneous and heterogeneous data distribution among the data holders. Our experimental evaluation shows that RHODE scales linearly with the number of data holders and the number of timesteps, sub-linearly and sub-quadratically with the number of features and the number of hidden units of RNNs, respectively. To the best of our knowledge, RHODE is the first system that provides the building blocks for the training of RNNs and its variants, under encryption in a federated learning setting.", "authors": ["Sinem Sav", "Abdulrahman Diaa", "Apostolos Pyrgelis", "Jean-Philippe Bossuat", "Jean-Pierre Hubaux"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2022-07-28", "url": "https://arxiv.org/abs/2207.13947", "pdf_url": "https://arxiv.org/pdf/2207.13947v2", "arxiv_id": "2207.13947", "doi": "10.48550/arXiv.2207.13947", "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Proceedings on Privacy Enhancing Technologies", "quality_score": 0.2698}
{"id": "c715eb9cc32affe45cd59e6571a50d538f505f17c8d427b66f9cdb87f1c12e55", "sources": ["arxiv", "semantic_scholar"], "title": "Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM", "abstract": "Federated learning (FL) enables distributed resource-constrained devices to jointly train shared models while keeping the training data local for privacy purposes. Vertical FL (VFL), which allows each client to collect partial features, has attracted intensive research efforts recently. We identified the main challenges that existing VFL frameworks are facing: the server needs to communicate gradients with the clients for each training step, incurring high communication cost that leads to rapid consumption of privacy budgets. To address these challenges, in this paper, we introduce a VFL framework with multiple heads (VIM), which takes the separate contribution of each client into account, and enables an efficient decomposition of the VFL optimization objective to sub-objectives that can be iteratively tackled by the server and the clients on their own. In particular, we propose an Alternating Direction Method of Multipliers (ADMM)-based method to solve our optimization problem, which allows clients to conduct multiple local updates before communication, and thus reduces the communication cost and leads to better performance under differential privacy (DP). We provide the user-level DP mechanism for our framework to protect user privacy. Moreover, we show that a byproduct of VIM is that the weights of learned heads reflect the importance of local clients. We conduct extensive evaluations and show that on four vertical FL datasets, VIM achieves significantly higher performance and faster convergence compared with the state-of-the-art. We also explicitly evaluate the importance of local clients and show that VIM enables functionalities such as client-level explanation and client denoising. We hope this work will shed light on a new way of effective VFL training and understanding.", "authors": ["Chulin Xie", "Pin-Yu Chen", "Qinbin Li", "Arash Nourian", "Ce Zhang", "Bo Li"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2022-07-20", "url": "https://arxiv.org/abs/2207.10226", "pdf_url": "https://arxiv.org/pdf/2207.10226v4", "arxiv_id": "2207.10226", "doi": "10.1109/SaTML59370.2024.00029", "citation_count": 23, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3451}
{"id": "4af06b6e1a3791f174f6c20de3e2bc825fcc8f35ab2e8c138bebd0041fbc5da0", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Self-Supervised Learning in Heterogeneous Settings: Limits of a Baseline Approach on HAR", "abstract": "Federated Learning is a new machine learning paradigm dealing with distributed model learning on independent devices. One of the many advantages of federated learning is that training data stay on devices (such as smartphones), and only learned models are shared with a centralized server. In the case of supervised learning, labeling is entrusted to the clients. However, acquiring such labels can be prohibitively expensive and error-prone for many tasks, such as human activity recognition. Hence, a wealth of data remains unlabelled and unexploited. Most existing federated learning approaches that focus mainly on supervised learning have mostly ignored this mass of unlabelled data. Furthermore, it is unclear whether standard federated Learning approaches are suited to self-supervised learning. The few studies that have dealt with the problem have limited themselves to the favorable situation of homogeneous datasets. This work lays the groundwork for a reference evaluation of federated Learning with Semi-Supervised Learning in a realistic setting. We show that standard lightweight autoencoder and standard Federated Averaging fail to learn a robust representation for Human Activity Recognition with several realistic heterogeneous datasets. These findings advocate for a more intensive research effort in Federated Self Supervised Learning to exploit the mass of heterogeneous unlabelled data present on mobile devices.", "authors": ["Sannara Ek", "Romain Rombourg", "François Portet", "Philippe Lalanda"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2022-07-17", "url": "https://arxiv.org/abs/2207.08187", "pdf_url": "https://arxiv.org/pdf/2207.08187v1", "arxiv_id": "2207.08187", "doi": "10.1109/PerComWorkshops53856.2022.9767369", "citation_count": 10, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2603}
{"id": "a1ecc5222a4e05d98931d59c416473d5810eb056746ada5bb1d0b62dea709202", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Continual Learning through distillation in pervasive computing", "abstract": "Federated Learning has been introduced as a new machine learning paradigm enhancing the use of local devices. At a server level, FL regularly aggregates models learned locally on distributed clients to obtain a more general model. Current solutions rely on the availability of large amounts of stored data at the client side in order to fine-tune the models sent by the server. Such setting is not realistic in mobile pervasive computing where data storage must be kept low and data characteristic can change dramatically. To account for this variability, a solution is to use the data regularly collected by the client to progressively adapt the received model. But such naive approach exposes clients to the well-known problem of catastrophic forgetting. To address this problem, we have defined a Federated Continual Learning approach which is mainly based on distillation. Our approach allows a better use of resources, eliminating the need to retrain from scratch at the arrival of new data and reducing memory usage by limiting the amount of data to be stored. This proposal has been evaluated in the Human Activity Recognition (HAR) domain and has shown to effectively reduce the catastrophic forgetting effect.", "authors": ["Anastasiia Usmanova", "François Portet", "Philippe Lalanda", "German Vega"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2022-07-17", "url": "https://arxiv.org/abs/2207.08181", "pdf_url": "https://arxiv.org/pdf/2207.08181v1", "arxiv_id": "2207.08181", "doi": "10.1109/smartcomp55677.2022.00027", "citation_count": 15, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "International Conference on Smart Computing", "quality_score": 0.301}
{"id": "56980bf948ac238833d0f10d28ad9e9f6acf9b7d3447d6117ead199a41f663d9", "sources": ["arxiv", "semantic_scholar"], "title": "Enhanced Security and Privacy via Fragmented Federated Learning", "abstract": "In federated learning (FL), a set of participants share updates computed on their local data with an aggregator server that combines updates into a global model. However, reconciling accuracy with privacy and security is a challenge to FL. On the one hand, good updates sent by honest participants may reveal their private local information, whereas poisoned updates sent by malicious participants may compromise the model's availability and/or integrity. On the other hand, enhancing privacy via update distortion damages accuracy, whereas doing so via update aggregation damages security because it does not allow the server to filter out individual poisoned updates. To tackle the accuracy-privacy-security conflict, we propose {\\em fragmented federated learning} (FFL), in which participants randomly exchange and mix fragments of their updates before sending them to the server. To achieve privacy, we design a lightweight protocol that allows participants to privately exchange and mix encrypted fragments of their updates so that the server can neither obtain individual updates nor link them to their originators. To achieve security, we design a reputation-based defense tailored for FFL that builds trust in participants and their mixed updates based on the quality of the fragments they exchange and the mixed updates they send. Since the exchanged fragments' parameters keep their original coordinates and attackers can be neutralized, the server can correctly reconstruct a global model from the received mixed updates without accuracy loss. Experiments on four real data sets show that FFL can prevent semi-honest servers from mounting privacy attacks, can effectively counter poisoning attacks and can keep the accuracy of the global model.", "authors": ["Najeeb Moharram Jebreel", "Josep Domingo-Ferrer", "Alberto Blanco-Justicia", "David Sanchez"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2022-07-13", "url": "https://arxiv.org/abs/2207.05978", "pdf_url": "https://arxiv.org/pdf/2207.05978v2", "arxiv_id": "2207.05978", "doi": "10.1109/TNNLS.2022.3212627", "citation_count": 45, "influential_citation_count": 5, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Neural Networks and Learning Systems", "quality_score": 0.4157}
{"id": "52d053434ac0ff78ebfb9f85295f086adef7f28ff6504170bd86f9204050641e", "sources": ["arxiv", "semantic_scholar"], "title": "Efficient and Privacy Preserving Group Signature for Federated Learning", "abstract": "Federated Learning (FL) is a Machine Learning (ML) technique that aims to reduce the threats to user data privacy. Training is done using the raw data on the users' device, called clients, and only the training results, called gradients, are sent to the server to be aggregated and generate an updated model. However, we cannot assume that the server can be trusted with private information, such as metadata related to the owner or source of the data. So, hiding the client information from the server helps reduce privacy-related attacks. Therefore, the privacy of the client's identity, along with the privacy of the client's data, is necessary to make such attacks more difficult. This paper proposes an efficient and privacy-preserving protocol for FL based on group signature. A new group signature for federated learning, called GSFL, is designed to not only protect the privacy of the client's data and identity but also significantly reduce the computation and communication costs considering the iterative process of federated learning. We show that GSFL outperforms existing approaches in terms of computation, communication, and signaling costs. Also, we show that the proposed protocol can handle various security attacks in the federated learning environment.", "authors": ["Sneha Kanchan", "Jae Won Jang", "Jun Yong Yoon", "Bong Jun Choi"], "categories": ["cs.CR", "cs.LG", "cs.NI"], "fields_of_study": ["Computer Science"], "published_date": "2022-07-12", "url": "https://arxiv.org/abs/2207.05297", "pdf_url": "https://arxiv.org/pdf/2207.05297v2", "arxiv_id": "2207.05297", "doi": "10.48550/arXiv.2207.05297", "citation_count": 19, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Future generations computer systems", "quality_score": 0.3253}
{"id": "b7e4e6d0738b28edbfe1247ebf256820e1f04dfe5366e247af944842494bb64d", "sources": ["arxiv", "semantic_scholar"], "title": "Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms", "abstract": "The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning is rapidly evolving to make wider use more practical. One of the most significant advancements in this domain is the incorporation of transfer learning into federated learning, which overcomes fundamental constraints of primary federated learning, particularly in terms of security. This chapter performs a comprehensive survey on the intersection of federated and transfer learning from a security point of view. The main goal of this study is to uncover potential vulnerabilities and defense mechanisms that might compromise the privacy and performance of systems that use federated and transfer learning.", "authors": ["Ehsan Hallaji", "Roozbeh Razavi-Far", "Mehrdad Saif"], "categories": ["cs.LG", "cs.AI", "cs.CR", "cs.CV", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2022-07-05", "url": "https://arxiv.org/abs/2207.02337", "pdf_url": "https://arxiv.org/pdf/2207.02337v1", "arxiv_id": "2207.02337", "doi": "10.1007/978-3-031-11748-0_3", "citation_count": 16, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3076}
{"id": "f0946ba12781b05921a58eb580c677242cda188138bc4f7f703b13eca618d4de", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Federated Long-Tailed Learning", "abstract": "Data privacy and class imbalance are the norm rather than the exception in many machine learning tasks. Recent attempts have been launched to, on one side, address the problem of learning from pervasive private data, and on the other side, learn from long-tailed data. However, both assumptions might hold in practical applications, while an effective method to simultaneously alleviate both issues is yet under development. In this paper, we focus on learning with long-tailed (LT) data distributions under the context of the popular privacy-preserved federated learning (FL) framework. We characterize three scenarios with different local or global long-tailed data distributions in the FL framework, and highlight the corresponding challenges. The preliminary results under different scenarios reveal that substantial future work are of high necessity to better resolve the characterized federated long-tailed learning tasks.", "authors": ["Zihan Chen", "Songshang Liu", "Hualiang Wang", "Howard H. Yang", "Tony Q. S. Quek", "Zuozhu Liu"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2022-06-30", "url": "https://arxiv.org/abs/2206.14988", "pdf_url": "https://arxiv.org/pdf/2206.14988v1", "arxiv_id": "2206.14988", "doi": "10.48550/arXiv.2206.14988", "citation_count": 15, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.301}
{"id": "4839305255956c49dac710d77ddfa478032f54b2a8b2a98c5822e01907371bb9", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-preserving Graph Analytics: Secure Generation and Federated Learning", "abstract": "Directly motivated by security-related applications from the Homeland Security Enterprise, we focus on the privacy-preserving analysis of graph data, which provides the crucial capacity to represent rich attributes and relationships. In particular, we discuss two directions, namely privacy-preserving graph generation and federated graph learning, which can jointly enable the collaboration among multiple parties each possessing private graph data. For each direction, we identify both \"quick wins\" and \"hard problems\". Towards the end, we demonstrate a user interface that can facilitate model explanation, interpretation, and visualization. We believe that the techniques developed in these directions will significantly enhance the capabilities of the Homeland Security Enterprise to tackle and mitigate the various security risks.", "authors": ["Dongqi Fu", "Jingrui He", "Hanghang Tong", "Ross Maciejewski"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-06-30", "url": "https://arxiv.org/abs/2207.00048", "pdf_url": "https://arxiv.org/pdf/2207.00048v1", "arxiv_id": "2207.00048", "doi": "10.48550/arXiv.2207.00048", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "cb01a8212a844954ec8cf450d7e9950b01e3dca2baa22469a94d5d64ebe4d5f2", "sources": ["arxiv", "semantic_scholar"], "title": "Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning", "abstract": "An oft-cited challenge of federated learning is the presence of heterogeneity. \\emph{Data heterogeneity} refers to the fact that data from different clients may follow very different distributions. \\emph{System heterogeneity} refers to client devices having different system capabilities. A considerable number of federated optimization methods address this challenge. In the literature, empirical evaluations usually start federated training from random initialization. However, in many practical applications of federated learning, the server has access to proxy data for the training task that can be used to pre-train a model before starting federated training. Using four standard federated learning benchmark datasets, we empirically study the impact of starting from a pre-trained model in federated learning. Unsurprisingly, starting from a pre-trained model reduces the training time required to reach a target error rate and enables the training of more accurate models (up to 40\\%) than is possible when starting from random initialization. Surprisingly, we also find that starting federated learning from a pre-trained initialization reduces the effect of both data and system heterogeneity. We recommend future work proposing and evaluating federated optimization methods to evaluate the performance when starting from random and pre-trained initializations. This study raises several questions for further work on understanding the role of heterogeneity in federated optimization. \\footnote{Our code is available at: \\url{https://github.com/facebookresearch/where_to_begin}}", "authors": ["John Nguyen", "Jianyu Wang", "Kshitiz Malik", "Maziar Sanjabi", "Michael Rabbat"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2022-06-30", "url": "https://arxiv.org/abs/2206.15387", "pdf_url": "https://arxiv.org/pdf/2206.15387v3", "arxiv_id": "2206.15387", "doi": null, "citation_count": 20, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/facebookresearch/where_to_begin}}", "venue": "International Conference on Learning Representations 2023", "quality_score": 0.3306}
{"id": "36016b4b4e12e09cac04d2793f6606a3bc0ca66091702437d40f8dacd1c0965e", "sources": ["arxiv", "semantic_scholar"], "title": "FLVoogd: Robust And Privacy Preserving Federated Learning", "abstract": "In this work, we propose FLVoogd, an updated federated learning method in which servers and clients collaboratively eliminate Byzantine attacks while preserving privacy. In particular, servers use automatic Density-based Spatial Clustering of Applications with Noise (DBSCAN) combined with S2PC to cluster the benign majority without acquiring sensitive personal information. Meanwhile, clients build dual models and perform test-based distance controlling to adjust their local models toward the global one to achieve personalizing. Our framework is automatic and adaptive that servers/clients don't need to tune the parameters during the training. In addition, our framework leverages Secure Multi-party Computation (SMPC) operations, including multiplications, additions, and comparison, where costly operations, like division and square root, are not required. Evaluations are carried out on some conventional datasets from the image classification field. The result shows that FLVoogd can effectively reject malicious uploads in most scenarios; meanwhile, it avoids data leakage from the server-side.", "authors": ["Yuhang Tian", "Rui Wang", "Yanqi Qiao", "Emmanouil Panaousis", "Kaitai Liang"], "categories": ["cs.CR", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-06-24", "url": "https://arxiv.org/abs/2207.00428", "pdf_url": "https://arxiv.org/pdf/2207.00428v1", "arxiv_id": "2207.00428", "doi": "10.48550/arXiv.2207.00428", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Asian Conference on Machine Learning", "quality_score": 0.1945}
{"id": "1c2a1fc0bd291ab5cc390b6048b9bd91ade5336179b40cd563c2cbed316b444a", "sources": ["arxiv", "semantic_scholar"], "title": "FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis", "abstract": "In the medical field, multi-center collaborations are often sought to yield more generalizable findings by leveraging the heterogeneity of patient and clinical data. However, recent privacy regulations hinder the possibility to share data, and consequently, to come up with machine learning-based solutions that support diagnosis and prognosis. Federated learning (FL) aims at sidestepping this limitation by bringing AI-based solutions to data owners and only sharing local AI models, or parts thereof, that need then to be aggregated. However, most of the existing federated learning solutions are still at their infancy and show several shortcomings, from the lack of a reliable and effective aggregation scheme able to retain the knowledge learned locally to weak privacy preservation as real data may be reconstructed from model updates. Furthermore, the majority of these approaches, especially those dealing with medical data, relies on a centralized distributed learning strategy that poses robustness, scalability and trust issues. In this paper we present a federated and decentralized learning strategy, FedER, that, exploiting experience replay and generative adversarial concepts, effectively integrates features from local nodes, providing models able to generalize across multiple datasets while maintaining privacy. FedER is tested on two tasks -- tuberculosis and melanoma classification -- using multiple datasets in order to simulate realistic non-i.i.d. medical data scenarios. Results show that our approach achieves performance comparable to standard (non-federated) learning and significantly outperforms state-of-the-art federated methods in their centralized (thus, more favourable) formulation. Code is available at https://github.com/perceivelab/FedER", "authors": ["Matteo Pennisi", "Federica Proietto Salanitri", "Giovanni Bellitto", "Bruno Casella", "Marco Aldinucci", "Simone Palazzo", "Concetto Spampinato"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2022-06-20", "url": "https://arxiv.org/abs/2206.10048", "pdf_url": "https://arxiv.org/pdf/2206.10048v3", "arxiv_id": "2206.10048", "doi": "10.1016/j.cviu.2023.103882", "citation_count": 20, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/perceivelab/FedER", "venue": "Computer Vision and Image Understanding", "quality_score": 0.3306}
{"id": "b3000dddd61abe7db51b997d9f8661f1c1aa2a30795561051c6ffc20a1d7626b", "sources": ["arxiv", "semantic_scholar"], "title": "Adaptive Expert Models for Personalization in Federated Learning", "abstract": "Federated Learning (FL) is a promising framework for distributed learning when data is private and sensitive. However, the state-of-the-art solutions in this framework are not optimal when data is heterogeneous and non-Independent and Identically Distributed (non-IID). We propose a practical and robust approach to personalization in FL that adjusts to heterogeneous and non-IID data by balancing exploration and exploitation of several global models. To achieve our aim of personalization, we use a Mixture of Experts (MoE) that learns to group clients that are similar to each other, while using the global models more efficiently. We show that our approach achieves an accuracy up to 29.78 % and up to 4.38 % better compared to a local model in a pathological non-IID setting, even though we tune our approach in the IID setting.", "authors": ["Martin Isaksson", "Edvin Listo Zec", "Rickard Cöster", "Daniel Gillblad", "Šarūnas Girdzijauskas"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-06-15", "url": "https://arxiv.org/abs/2206.07832", "pdf_url": "https://arxiv.org/pdf/2206.07832v1", "arxiv_id": "2206.07832", "doi": "10.48550/arXiv.2206.07832", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2258}
{"id": "255f06c90bfb80f8f24b9b8856566c3c34274b72aa4554e0f5664e6b0c352b97", "sources": ["arxiv", "semantic_scholar"], "title": "MammoFL: Mammographic Breast Density Estimation using Federated Learning", "abstract": "In this study, we automate quantitative mammographic breast density estimation with neural networks and show that this tool is a strong use case for federated learning on multi-institutional datasets. Our dataset included bilateral CC-view and MLO-view mammographic images from two separate institutions. Two U-Nets were separately trained on algorithm-generated labels to perform segmentation of the breast and dense tissue from these images and subsequently calculate breast percent density (PD). The networks were trained with federated learning and compared to three non-federated baselines, one trained on each single-institution dataset and one trained on the aggregated multi-institution dataset. We demonstrate that training on multi-institution datasets is critical to algorithm generalizability. We further show that federated learning on multi-institutional datasets improves model generalization to unseen data at nearly the same level as centralized training on multi-institutional datasets, indicating that federated learning can be applied to our method to improve algorithm generalizability while maintaining patient privacy.", "authors": ["Ramya Muthukrishnan", "Angelina Heyler", "Keshava Katti", "Sarthak Pati", "Walter Mankowski", "Aprupa Alahari", "Michael Sanborn", "Emily F. Conant", "Christopher Scott", "Stacey Winham", "Celine Vachon", "Pratik Chaudhari", "Despina Kontos", "Spyridon Bakas"], "categories": ["eess.IV", "cs.CV", "cs.DC", "cs.LG"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2022-06-11", "url": "https://arxiv.org/abs/2206.05575", "pdf_url": "https://arxiv.org/pdf/2206.05575v5", "arxiv_id": "2206.05575", "doi": null, "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1747}
{"id": "2000ce37acfc77ac6f9928cb1288eaaa7004d7702f9ec4ec38902622fe31d745", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning with GAN-based Data Synthesis for Non-IID Clients", "abstract": "Federated learning (FL) has recently emerged as a popular privacy-preserving collaborative learning paradigm. However, it suffers from the non-independent and identically distributed (non-IID) data among clients. In this paper, we propose a novel framework, named Synthetic Data Aided Federated Learning (SDA-FL), to resolve this non-IID challenge by sharing synthetic data. Specifically, each client pretrains a local generative adversarial network (GAN) to generate differentially private synthetic data, which are uploaded to the parameter server (PS) to construct a global shared synthetic dataset. To generate confident pseudo labels for the synthetic dataset, we also propose an iterative pseudo labeling mechanism performed by the PS. A combination of the local private dataset and synthetic dataset with confident pseudo labels leads to nearly identical data distributions among clients, which improves the consistency among local models and benefits the global aggregation. Extensive experiments evidence that the proposed framework outperforms the baseline methods by a large margin in several benchmark datasets under both the supervised and semi-supervised settings.", "authors": ["Zijian Li", "Jiawei Shao", "Yuyi Mao", "Jessie Hui Wang", "Jun Zhang"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-06-11", "url": "https://arxiv.org/abs/2206.05507", "pdf_url": "https://arxiv.org/pdf/2206.05507v1", "arxiv_id": "2206.05507", "doi": "10.48550/arXiv.2206.05507", "citation_count": 64, "influential_citation_count": 9, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.5}
{"id": "2ea81fe9903ae2b27d3f20d8d713c48eb69057acbda4438f21278cf3285b4f35", "sources": ["arxiv", "semantic_scholar"], "title": "Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurance", "abstract": "Machine learning abilities have become a vital component for various solutions across industries, applications, and sectors. Many organizations seek to leverage AI-based solutions across their business services to unlock better efficiency and increase productivity. Problems, however, can arise if there is a lack of quality data for AI-model training, scalability, and maintenance. We propose a data-centric federated learning architecture leveraged by a public blockchain and smart contracts to overcome this significant issue. Our proposed solution provides a virtual public marketplace where developers, data scientists, and AI-engineer can publish their models and collaboratively create and access quality data for training. We enhance data quality and integrity through an incentive mechanism that rewards contributors for data contribution and verification. Those combined with the proposed framework helped increase with only one user simulation the training dataset with an average of 100 input daily and the model accuracy by approximately 4\\%.", "authors": ["Riadh Ben Chaabene", "Darine Amayed", "Mohamed Cheriet"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2022-06-09", "url": "https://arxiv.org/abs/2206.04731", "pdf_url": "https://arxiv.org/pdf/2206.04731v1", "arxiv_id": "2206.04731", "doi": "10.48550/arXiv.2206.04731", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "3e8cb4bee575001ff4b6ef42b998980530d2a328c40499c0b0111f70bc6bde6c", "sources": ["arxiv", "semantic_scholar"], "title": "Certified Robustness in Federated Learning", "abstract": "Federated learning has recently gained significant attention and popularity due to its effectiveness in training machine learning models on distributed data privately. However, as in the single-node supervised learning setup, models trained in federated learning suffer from vulnerability to imperceptible input transformations known as adversarial attacks, questioning their deployment in security-related applications. In this work, we study the interplay between federated training, personalization, and certified robustness. In particular, we deploy randomized smoothing, a widely-used and scalable certification method, to certify deep networks trained on a federated setup against input perturbations and transformations. We find that the simple federated averaging technique is effective in building not only more accurate, but also more certifiably-robust models, compared to training solely on local data. We further analyze personalization, a popular technique in federated training that increases the model's bias towards local data, on robustness. We show several advantages of personalization over both~(that is, only training on local data and federated training) in building more robust models with faster training. Finally, we explore the robustness of mixtures of global and local~(i.e. personalized) models, and find that the robustness of local models degrades as they diverge from the global model", "authors": ["Motasem Alfarra", "Juan C. Pérez", "Egor Shulgin", "Peter Richtárik", "Bernard Ghanem"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-06-06", "url": "https://arxiv.org/abs/2206.02535", "pdf_url": "https://arxiv.org/pdf/2206.02535v2", "arxiv_id": "2206.02535", "doi": "10.48550/arXiv.2206.02535", "citation_count": 10, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2603}
{"id": "f7a919f815d5df9115d63d5c28cdeeb210b98d833f12568bd260b7be31e597d6", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data", "abstract": "Federated learning (FL) is a privacy-promoting framework that enables potentially large number of clients to collaboratively train machine learning models. In a FL system, a server coordinates the collaboration by collecting and aggregating clients' model updates while the clients' data remains local and private. A major challenge in federated learning arises when the local data is heterogeneous -- the setting in which performance of the learned global model may deteriorate significantly compared to the scenario where the data is identically distributed across the clients. In this paper we propose FedDPMS (Federated Differentially Private Means Sharing), an FL algorithm in which clients deploy variational auto-encoders to augment local datasets with data synthesized using differentially private means of latent data representations communicated by a trusted server. Such augmentation ameliorates effects of data heterogeneity across the clients without compromising privacy. Our experiments on deep image classification tasks demonstrate that FedDPMS outperforms competing state-of-the-art FL methods specifically designed for heterogeneous data settings.", "authors": ["Huancheng Chen", "Haris Vikalo"], "categories": ["cs.LG", "cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2022-06-01", "url": "https://arxiv.org/abs/2206.00686", "pdf_url": "https://arxiv.org/pdf/2206.00686v2", "arxiv_id": "2206.00686", "doi": "10.1109/CVPRW59228.2023.00531", "citation_count": 23, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3451}
{"id": "79c510ef6a14af45e9d3025025ca52e41c64af7a6fe88570482c25aed536b928", "sources": ["arxiv", "semantic_scholar"], "title": "LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation", "abstract": "In Federated Learning, it is crucial to handle low-quality, corrupted, or malicious data. However, traditional data valuation methods are not suitable due to privacy concerns. To address this, we propose a simple yet effective approach that utilizes a new influence approximation called \"lazy influence\" to filter and score data while preserving privacy. To do this, each participant uses their own data to estimate the influence of another participant's batch and sends a differentially private obfuscated score to the central coordinator. Our method has been shown to successfully filter out biased and corrupted data in various simulated and real-world settings, achieving a recall rate of over $>90\\%$ (sometimes up to $100\\%$) while maintaining strong differential privacy guarantees with $\\varepsilon \\leq 1$.", "authors": ["Ljubomir Rokvic", "Panayiotis Danassis", "Sai Praneeth Karimireddy", "Boi Faltings"], "categories": ["cs.CR", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-05-23", "url": "https://arxiv.org/abs/2205.11518", "pdf_url": "https://arxiv.org/pdf/2205.11518v4", "arxiv_id": "2205.11518", "doi": "10.1109/BigData62323.2024.10825097", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.1505}
{"id": "2f4ac312053d98f701954b5314fc29efccb32f1318d9d5d334d90784439ec3b0", "sources": ["arxiv", "semantic_scholar"], "title": "FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders", "abstract": "Face recognition, as one of the most successful applications in artificial intelligence, has been widely used in security, administration, advertising, and healthcare. However, the privacy issues of public face datasets have attracted increasing attention in recent years. Previous works simply mask most areas of faces or synthesize samples using generative models to construct privacy-preserving face datasets, which overlooks the trade-off between privacy protection and data utility. In this paper, we propose a novel framework FaceMAE, where the face privacy and recognition performance are considered simultaneously. Firstly, randomly masked face images are used to train the reconstruction module in FaceMAE. We tailor the instance relation matching (IRM) module to minimize the distribution gap between real faces and FaceMAE reconstructed ones. During the deployment phase, we use trained FaceMAE to reconstruct images from masked faces of unseen identities without extra training. The risk of privacy leakage is measured based on face retrieval between reconstructed and original datasets. Experiments prove that the identities of reconstructed images are difficult to be retrieved. We also perform sufficient privacy-preserving face recognition on several public face datasets (i.e. CASIA-WebFace and WebFace260M). Compared to previous state of the arts, FaceMAE consistently \\textbf{reduces at least 50\\% error rate} on LFW, CFP-FP and AgeDB.", "authors": ["Kai Wang", "Bo Zhao", "Xiangyu Peng", "Zheng Zhu", "Jiankang Deng", "Xinchao Wang", "Hakan Bilen", "Yang You"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2022-05-23", "url": "https://arxiv.org/abs/2205.11090", "pdf_url": "https://arxiv.org/pdf/2205.11090v1", "arxiv_id": "2205.11090", "doi": "10.48550/arXiv.2205.11090", "citation_count": 12, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2785}
{"id": "85c531ddee786e57de0f8e67051354ef27f1d5721cad6230f39cc0d1abab7aff", "sources": ["arxiv", "semantic_scholar"], "title": "Transfer Learning with Pre-trained Conditional Generative Models", "abstract": "Transfer learning is crucial in training deep neural networks on new target tasks. Current transfer learning methods always assume at least one of (i) source and target task label spaces overlap, (ii) source datasets are available, and (iii) target network architectures are consistent with source ones. However, holding these assumptions is difficult in practical settings because the target task rarely has the same labels as the source task, the source dataset access is restricted due to storage costs and privacy, and the target architecture is often specialized to each task. To transfer source knowledge without these assumptions, we propose a transfer learning method that uses deep generative models and is composed of the following two stages: pseudo pre-training (PP) and pseudo semi-supervised learning (P-SSL). PP trains a target architecture with an artificial dataset synthesized by using conditional source generative models. P-SSL applies SSL algorithms to labeled target data and unlabeled pseudo samples, which are generated by cascading the source classifier and generative models to condition them with target samples. Our experimental results indicate that our method can outperform the baselines of scratch training and knowledge distillation.", "authors": ["Shin'ya Yamaguchi", "Sekitoshi Kanai", "Atsutoshi Kumagai", "Daiki Chijiwa", "Hisashi Kashima"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2022-04-27", "url": "https://arxiv.org/abs/2204.12833", "pdf_url": "https://arxiv.org/pdf/2204.12833v3", "arxiv_id": "2204.12833", "doi": "10.1007/s10994-025-06748-7", "citation_count": 7, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Machine-mediated learning", "quality_score": 0.2258}
{"id": "bf298b5a18eefc774475c0674a46745716a516a633d2b816bc9962a4735faa8c", "sources": ["arxiv", "semantic_scholar"], "title": "Secure Distributed/Federated Learning: Prediction-Privacy Trade-Off for Multi-Agent System", "abstract": "Decentralized learning is an efficient emerging paradigm for boosting the computing capability of multiple bounded computing agents. In the big data era, performing inference within the distributed and federated learning (DL and FL) frameworks, the central server needs to process a large amount of data while relying on various agents to perform multiple distributed training tasks. Considering the decentralized computing topology, privacy has become a first-class concern. Moreover, assuming limited information processing capability for the agents calls for a sophisticated \\textit{privacy-preserving decentralization} that ensures efficient computation. Towards this end, we study the \\textit{privacy-aware server to multi-agent assignment} problem subject to information processing constraints associated with each agent, while maintaining the privacy and assuring learning informative messages received by agents about a global terminal through the distributed private federated learning (DPFL) approach. To find a decentralized scheme for a two-agent system, we formulate an optimization problem that balances privacy and accuracy, taking into account the quality of compression constraints associated with each agent. We propose an iterative converging algorithm by alternating over self-consistent equations. We also numerically evaluate the proposed solution to show the privacy-prediction trade-off and demonstrate the efficacy of the novel approach in ensuring privacy in DL and FL.", "authors": ["Mohamed Ridha Znaidi", "Gaurav Gupta", "Paul Bogdan"], "categories": ["cs.MA", "cs.CR", "cs.IT", "cs.LG"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2022-04-24", "url": "https://arxiv.org/abs/2205.04855", "pdf_url": "https://arxiv.org/pdf/2205.04855v1", "arxiv_id": "2205.04855", "doi": "10.48550/arXiv.2205.04855", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Symposium on Information Theory", "quality_score": 0.1193}
{"id": "fad7bee52d3e58b8c9caf34c695f9c15d4ee6d571b84b929dc991263bb051bb1", "sources": ["arxiv", "semantic_scholar"], "title": "FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation", "abstract": "Contrastive learning is widely used for recommendation model learning, where selecting representative and informative negative samples is critical. Existing methods usually focus on centralized data, where abundant and high-quality negative samples are easy to obtain. However, centralized user data storage and exploitation may lead to privacy risks and concerns, while decentralized user data on a single client can be too sparse and biased for accurate contrastive learning. In this paper, we propose a federated contrastive learning method named FedCL for privacy-preserving recommendation, which can exploit high-quality negative samples for effective model training with privacy well protected. We first infer user embeddings from local user data through the local model on each client, and then perturb them with local differential privacy (LDP) before sending them to a central server for hard negative sampling. Since individual user embedding contains heavy noise due to LDP, we propose to cluster user embeddings on the server to mitigate the influence of noise, and the cluster centroids are used to retrieve hard negative samples from the item pool. These hard negative samples are delivered to user clients and mixed with the observed negative samples from local data as well as in-batch negatives constructed from positive samples for federated model training. Extensive experiments on four benchmark datasets show FedCL can empower various recommendation methods in a privacy-preserving way.", "authors": ["Chuhan Wu", "Fangzhao Wu", "Tao Qi", "Yongfeng Huang", "Xing Xie"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-04-21", "url": "https://arxiv.org/abs/2204.09850", "pdf_url": "https://arxiv.org/pdf/2204.09850v1", "arxiv_id": "2204.09850", "doi": "10.48550/arXiv.2204.09850", "citation_count": 24, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3495}
{"id": "9a241d072f1ffe8f71ba8b5af21bbedc701c4903abbb4d70de6426aeb7b17c50", "sources": ["arxiv", "semantic_scholar"], "title": "Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case", "abstract": "Medical data is often highly sensitive in terms of data privacy and security concerns. Federated learning, one type of machine learning techniques, has been started to use for the improvement of the privacy and security of medical data. In the federated learning, the training data is distributed across multiple machines, and the learning process is performed in a collaborative manner. There are several privacy attacks on deep learning (DL) models to get the sensitive information by attackers. Therefore, the DL model itself should be protected from the adversarial attack, especially for applications using medical data. One of the solutions for this problem is homomorphic encryption-based model protection from the adversary collaborator. This paper proposes a privacy-preserving federated learning algorithm for medical data using homomorphic encryption. The proposed algorithm uses a secure multi-party computation protocol to protect the deep learning model from the adversaries. In this study, the proposed algorithm using a real-world medical dataset is evaluated in terms of the model performance.", "authors": ["Febrianti Wibawa", "Ferhat Ozgur Catak", "Salih Sarp", "Murat Kuzlu", "Umit Cali"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-04-16", "url": "https://arxiv.org/abs/2204.07752", "pdf_url": "https://arxiv.org/pdf/2204.07752v1", "arxiv_id": "2204.07752", "doi": "10.1145/3528580.3532845", "citation_count": 108, "influential_citation_count": 5, "has_code": false, "code_url": null, "venue": "European Interdisciplinary Cybersecurity Conference", "quality_score": 0.5094}
{"id": "d4ed64c5a7ce2322be24fd0435d091a652875295c065b9d5adb4777aa2d758cf", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Federated Learning via System Immersion and Random Matrix Encryption", "abstract": "Federated learning (FL) has emerged as a privacy solution for collaborative distributed learning where clients train AI models directly on their devices instead of sharing their data with a centralized (potentially adversarial) server. Although FL preserves local data privacy to some extent, it has been shown that information about clients' data can still be inferred from model updates. In recent years, various privacy-preserving schemes have been developed to address this privacy leakage. However, they often provide privacy at the expense of model performance or system efficiency, and balancing these tradeoffs is a crucial challenge when implementing FL schemes. In this manuscript, we propose a Privacy-Preserving Federated Learning (PPFL) framework built on the synergy of matrix encryption and system immersion tools from control theory. The idea is to immerse the learning algorithm, a Stochastic Gradient Decent (SGD), into a higher-dimensional system (the so-called target system) and design the dynamics of the target system so that: the trajectories of the original SGD are immersed/embedded in its trajectories, and it learns on encrypted data (here we use random matrix encryption). Matrix encryption is reformulated at the server as a random change of coordinates that maps original parameters to a higher-dimensional parameter space and enforces that the target SGD converges to an encrypted version of the original SGD optimal solution. The server decrypts the aggregated model using the left inverse of the immersion map. We show that our algorithm provides the same level of accuracy and convergence rate as the standard FL with a negligible computation cost while revealing no information about the clients' data.", "authors": ["Haleh Hayati", "Carlos Murguia", "Nathan van de Wouw"], "categories": ["cs.LG", "cs.CR", "eess.SY"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2022-04-05", "url": "https://arxiv.org/abs/2204.02497", "pdf_url": "https://arxiv.org/pdf/2204.02497v2", "arxiv_id": "2204.02497", "doi": "10.1109/CDC51059.2022.9993113", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Conference on Decision and Control", "quality_score": 0.2386}
{"id": "30cf41c56acf2dd7fb3e1e968ea383292fd08dda38cb35317ba3166199a5fcc3", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Aggregation in Federated Learning: A Survey", "abstract": "Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms and growing concerns over personal data privacy, Privacy-Preserving Federated Learning (PPFL) has attracted tremendous attention from both academia and industry. Practical PPFL typically allows multiple participants to individually train their machine learning models, which are then aggregated to construct a global model in a privacy-preserving manner. As such, Privacy-Preserving Aggregation (PPAgg) as the key protocol in PPFL has received substantial research interest. This survey aims to fill the gap between a large number of studies on PPFL, where PPAgg is adopted to provide a privacy guarantee, and the lack of a comprehensive survey on the PPAgg protocols applied in FL systems. In this survey, we review the PPAgg protocols proposed to address privacy and security issues in FL systems. The focus is placed on the construction of PPAgg protocols with an extensive analysis of the advantages and disadvantages of these selected PPAgg protocols and solutions. Additionally, we discuss the open-source FL frameworks that support PPAgg. Finally, we highlight important challenges and future research directions for applying PPAgg to FL systems and the combination of PPAgg with other technologies for further security improvement.", "authors": ["Ziyao Liu", "Jiale Guo", "Wenzhuo Yang", "Jiani Fan", "Kwok-Yan Lam", "Jun Zhao"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2022-03-31", "url": "https://arxiv.org/abs/2203.17005", "pdf_url": "https://arxiv.org/pdf/2203.17005v2", "arxiv_id": "2203.17005", "doi": "10.48550/arXiv.2203.17005", "citation_count": 159, "influential_citation_count": 8, "has_code": true, "code_url": null, "venue": "IEEE Transactions on Big Data", "quality_score": 0.551}
{"id": "1043d6ae602d23400cc47dc62295fa127c27ed75e76042ae6bcf0c5b1e08b43a", "sources": ["arxiv", "semantic_scholar"], "title": "FedVLN: Privacy-preserving Federated Vision-and-Language Navigation", "abstract": "Data privacy is a central problem for embodied agents that can perceive the environment, communicate with humans, and act in the real world. While helping humans complete tasks, the agent may observe and process sensitive information of users, such as house environments, human activities, etc. In this work, we introduce privacy-preserving embodied agent learning for the task of Vision-and-Language Navigation (VLN), where an embodied agent navigates house environments by following natural language instructions. We view each house environment as a local client, which shares nothing other than local updates with the cloud server and other clients, and propose a novel federated vision-and-language navigation (FedVLN) framework to protect data privacy during both training and pre-exploration. Particularly, we propose a decentralized training strategy to limit the data of each client to its local model training and a federated pre-exploration method to do partial model aggregation to improve model generalizability to unseen environments. Extensive results on R2R and RxR datasets show that under our FedVLN framework, decentralized VLN models achieve comparable results with centralized training while protecting seen environment privacy, and federated pre-exploration significantly outperforms centralized pre-exploration while preserving unseen environment privacy.", "authors": ["Kaiwen Zhou", "Xin Eric Wang"], "categories": ["cs.AI", "cs.CL", "cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-03-28", "url": "https://arxiv.org/abs/2203.14936", "pdf_url": "https://arxiv.org/pdf/2203.14936v3", "arxiv_id": "2203.14936", "doi": "10.48550/arXiv.2203.14936", "citation_count": 13, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "European Conference on Computer Vision", "quality_score": 0.2865}
{"id": "7779e02d918fdd4949b570bb9bc5bd8e582c1b2b8123ae70e45d7ba1c1060fb9", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey", "abstract": "Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare system by using IoMT devices. However, due to the centralized training approach of artificial intelligence (AI), the use of mobile and wearable IoMT devices raises privacy concerns with respect to the information that has been communicated between hospitals and end users. The information conveyed by the IoMT devices is highly confidential and can be exposed to adversaries. In this regard, federated learning (FL), a distributive AI paradigm has opened up new opportunities for privacy-preservation in IoMT without accessing the confidential data of the participants. Further, FL provides privacy to end users as only gradients are shared during training. For these specific properties of FL, in this paper we present privacy related issues in IoMT. Afterwards, we present the role of FL in IoMT networks for privacy preservation and introduce some advanced FL architectures incorporating deep reinforcement learning (DRL), digital twin, and generative adversarial networks (GANs) for detecting privacy threats. Subsequently, we present some practical opportunities of FL in smart healthcare systems. At the end, we conclude this survey by providing open research challenges for FL that can be used in future smart healthcare systems", "authors": ["Mansoor Ali", "Faisal Naeem", "Muhammad Tariq", "Geroges Kaddoum"], "categories": ["eess.SY", "cs.CR", "cs.LG"], "fields_of_study": ["Computer Science", "Engineering", "Medicine"], "published_date": "2022-03-18", "url": "https://arxiv.org/abs/2203.09702", "pdf_url": "https://arxiv.org/pdf/2203.09702v1", "arxiv_id": "2203.09702", "doi": "10.1109/JBHI.2022.3181823", "citation_count": 252, "influential_citation_count": 11, "has_code": false, "code_url": null, "venue": "IEEE journal of biomedical and health informatics", "quality_score": 0.6008}
{"id": "ce04e1ee3c3336291dd3614484d869086ec7289497a91042c71dd5737cff6aa9", "sources": ["arxiv", "semantic_scholar"], "title": "Acceleration of Federated Learning with Alleviated Forgetting in Local Training", "abstract": "Federated learning (FL) enables distributed optimization of machine learning models while protecting privacy by independently training local models on each client and then aggregating parameters on a central server, thereby producing an effective global model. Although a variety of FL algorithms have been proposed, their training efficiency remains low when the data are not independently and identically distributed (non-i.i.d.) across different clients. We observe that the slow convergence rates of the existing methods are (at least partially) caused by the catastrophic forgetting issue during the local training stage on each individual client, which leads to a large increase in the loss function concerning the previous training data at the other clients. Here, we propose FedReg, an algorithm to accelerate FL with alleviated knowledge forgetting in the local training stage by regularizing locally trained parameters with the loss on generated pseudo data, which encode the knowledge of previous training data learned by the global model. Our comprehensive experiments demonstrate that FedReg not only significantly improves the convergence rate of FL, especially when the neural network architecture is deep and the clients' data are extremely non-i.i.d., but is also able to protect privacy better in classification problems and more robust against gradient inversion attacks. The code is available at: https://github.com/Zoesgithub/FedReg.", "authors": ["Chencheng Xu", "Zhiwei Hong", "Minlie Huang", "Tao Jiang"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2022-03-05", "url": "https://arxiv.org/abs/2203.02645", "pdf_url": "https://arxiv.org/pdf/2203.02645v1", "arxiv_id": "2203.02645", "doi": "10.48550/arXiv.2203.02645", "citation_count": 64, "influential_citation_count": 4, "has_code": true, "code_url": "https://github.com/Zoesgithub/FedReg", "venue": "International Conference on Learning Representations", "quality_score": 0.4532}
{"id": "3171b864bb28e36b345f2f140f3a0296a02f2d2464637a69e5e19ab168e0fe08", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy Leakage of Adversarial Training Models in Federated Learning Systems", "abstract": "Adversarial Training (AT) is crucial for obtaining deep neural networks that are robust to adversarial attacks, yet recent works found that it could also make models more vulnerable to privacy attacks. In this work, we further reveal this unsettling property of AT by designing a novel privacy attack that is practically applicable to the privacy-sensitive Federated Learning (FL) systems. Using our method, the attacker can exploit AT models in the FL system to accurately reconstruct users' private training images even when the training batch size is large. Code is available at https://github.com/zjysteven/PrivayAttack_AT_FL.", "authors": ["Jingyang Zhang", "Yiran Chen", "Hai Li"], "categories": ["cs.LG", "cs.CR", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2022-02-21", "url": "https://arxiv.org/abs/2202.10546", "pdf_url": "https://arxiv.org/pdf/2202.10546v1", "arxiv_id": "2202.10546", "doi": "10.1109/CVPRW56347.2022.00021", "citation_count": 21, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/zjysteven/PrivayAttack_AT_FL", "venue": null, "quality_score": 0.3356}
{"id": "65830fd4bdf3d447b68cf2c90bd2a63ccd42d4bcc7911c3c033660b31451f8e6", "sources": ["arxiv", "semantic_scholar"], "title": "Collusion Resistant Federated Learning with Oblivious Distributed Differential Privacy", "abstract": "Privacy-preserving federated learning enables a population of distributed clients to jointly learn a shared model while keeping client training data private, even from an untrusted server. Prior works do not provide efficient solutions that protect against collusion attacks in which parties collaborate to expose an honest client's model parameters. We present an efficient mechanism based on oblivious distributed differential privacy that is the first to protect against such client collusion, including the \"Sybil\" attack in which a server preferentially selects compromised devices or simulates fake devices. We leverage the novel privacy mechanism to construct a secure federated learning protocol and prove the security of that protocol. We conclude with empirical analysis of the protocol's execution speed, learning accuracy, and privacy performance on two data sets within a realistic simulation of 5,000 distributed network clients.", "authors": ["David Byrd", "Vaikkunth Mugunthan", "Antigoni Polychroniadou", "Tucker Hybinette Balch"], "categories": ["cs.CR", "cs.AI", "cs.MA"], "fields_of_study": ["Computer Science"], "published_date": "2022-02-20", "url": "https://arxiv.org/abs/2202.09897", "pdf_url": "https://arxiv.org/pdf/2202.09897v1", "arxiv_id": "2202.09897", "doi": "10.1145/3533271.3561754", "citation_count": 13, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "International Conference on AI in Finance", "quality_score": 0.2865}
{"id": "2d637bc7822e31176b55dda568ccd78b7c9589b9cbe2afa06ec3474f76e5db50", "sources": ["arxiv", "semantic_scholar"], "title": "Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning", "abstract": "Large-scale machine learning systems often involve data distributed across a collection of users. Federated learning algorithms leverage this structure by communicating model updates to a central server, rather than entire datasets. In this paper, we study stochastic optimization algorithms for a personalized federated learning setting involving local and global models subject to user-level (joint) differential privacy. While learning a private global model induces a cost of privacy, local learning is perfectly private. We provide generalization guarantees showing that coordinating local learning with private centralized learning yields a generically useful and improved tradeoff between accuracy and privacy. We illustrate our theoretical results with experiments on synthetic and real-world datasets.", "authors": ["Alberto Bietti", "Chen-Yu Wei", "Miroslav Dudík", "John Langford", "Zhiwei Steven Wu"], "categories": ["stat.ML", "cs.CR", "cs.LG", "math.OC"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2022-02-10", "url": "https://arxiv.org/abs/2202.05318", "pdf_url": "https://arxiv.org/pdf/2202.05318v2", "arxiv_id": "2202.05318", "doi": null, "citation_count": 69, "influential_citation_count": 6, "has_code": false, "code_url": null, "venue": "International Conference on Machine Learning", "quality_score": 0.4613}
{"id": "2e3f99e1a5886fe2a630e7d7a298c2179f62581850bbffa7354dd19651440159", "sources": ["arxiv", "semantic_scholar"], "title": "APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning", "abstract": "Federated learning (FL) enables training models at different sites and updating the weights from the training instead of transferring data to a central location and training as in classical machine learning. The FL capability is especially important to domains such as biomedicine and smart grid, where data may not be shared freely or stored at a central location because of policy challenges. Thanks to the capability of learning from decentralized datasets, FL is now a rapidly growing research field, and numerous FL frameworks have been developed. In this work, we introduce APPFL, the Argonne Privacy-Preserving Federated Learning framework. APPFL allows users to leverage implemented privacy-preserving algorithms, implement new algorithms, and simulate and deploy various FL algorithms with privacy-preserving techniques. The modular framework enables users to customize the components for algorithms, privacy, communication protocols, neural network models, and user data. We also present a new communication-efficient algorithm based on an inexact alternating direction method of multipliers. The algorithm requires significantly less communication between the server and the clients than does the current state of the art. We demonstrate the computational capabilities of APPFL, including differentially private FL on various test datasets and its scalability, by using multiple algorithms and datasets on different computing environments.", "authors": ["Minseok Ryu", "Youngdae Kim", "Kibaek Kim", "Ravi K. Madduri"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-02-08", "url": "https://arxiv.org/abs/2202.03672", "pdf_url": "https://arxiv.org/pdf/2202.03672v2", "arxiv_id": "2202.03672", "doi": "10.1109/IPDPSW55747.2022.00175", "citation_count": 37, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum", "quality_score": 0.3949}
{"id": "7e6c9ad1668b1a88e0f9f4bab26e84cd31682db18d772c9082b1199838557417", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated Learning", "abstract": "Speech Emotion Recognition (SER) refers to the recognition of human emotions from natural speech. If done accurately, it can offer a number of benefits in building human-centered context-aware intelligent systems. Existing SER approaches are largely centralized, without considering users' privacy. Federated Learning (FL) is a distributed machine learning paradigm dealing with decentralization of privacy-sensitive personal data. In this paper, we present a privacy-preserving and data-efficient SER approach by utilizing the concept of FL. To the best of our knowledge, this is the first federated SER approach, which utilizes self-training learning in conjunction with federated learning to exploit both labeled and unlabeled on-device data. Our experimental evaluations on the IEMOCAP dataset shows that our federated approach can learn generalizable SER models even under low availability of data labels and highly non-i.i.d. distributions. We show that our approach with as few as 10% labeled data, on average, can improve the recognition rate by 8.67% compared to the fully-supervised federated counterparts.", "authors": ["Vasileios Tsouvalas", "Tanir Ozcelebi", "Nirvana Meratnia"], "categories": ["cs.LG", "cs.AI", "cs.SD", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2022-02-05", "url": "https://arxiv.org/abs/2202.02611", "pdf_url": "https://arxiv.org/pdf/2202.02611v1", "arxiv_id": "2202.02611", "doi": "10.1109/PerComWorkshops53856.2022.9767445", "citation_count": 40, "influential_citation_count": 5, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.4032}
{"id": "6ccbcfd1912b7fb895454973484b9ec2cf8f7649794cab2d45bff52eea4afc27", "sources": ["arxiv", "semantic_scholar"], "title": "Proportional Fairness in Federated Learning", "abstract": "With the increasingly broad deployment of federated learning (FL) systems in the real world, it is critical but challenging to ensure fairness in FL, i.e. reasonably satisfactory performances for each of the numerous diverse clients. In this work, we introduce and study a new fairness notion in FL, called proportional fairness (PF), which is based on the relative change of each client's performance. From its connection with the bargaining games, we propose PropFair, a novel and easy-to-implement algorithm for finding proportionally fair solutions in FL and study its convergence properties. Through extensive experiments on vision and language datasets, we demonstrate that PropFair can approximately find PF solutions, and it achieves a good balance between the average performances of all clients and of the worst 10% clients. Our code is available at \\url{https://github.com/huawei-noah/Federated-Learning/tree/main/FairFL}.", "authors": ["Guojun Zhang", "Saber Malekmohammadi", "Xi Chen", "Yaoliang Yu"], "categories": ["cs.LG", "cs.AI", "math.OC", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2022-02-03", "url": "https://arxiv.org/abs/2202.01666", "pdf_url": "https://arxiv.org/pdf/2202.01666v5", "arxiv_id": "2202.01666", "doi": null, "citation_count": 34, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/huawei-noah/Federated-Learning/tree/main/FairFL", "venue": null, "quality_score": 0.386}
{"id": "af265a967f14c62c92633836f329c47510a534c9f46727ace1273fc98e62bf8d", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning Challenges and Opportunities: An Outlook", "abstract": "Federated learning (FL) has been developed as a promising framework to leverage the resources of edge devices, enhance customers' privacy, comply with regulations, and reduce development costs. Although many methods and applications have been developed for FL, several critical challenges for practical FL systems remain unaddressed. This paper provides an outlook on FL development, categorized into five emerging directions of FL, namely algorithm foundation, personalization, hardware and security constraints, lifelong learning, and nonstandard data. Our unique perspectives are backed by practical observations from large-scale federated systems for edge devices.", "authors": ["Jie Ding", "Eric Tramel", "Anit Kumar Sahu", "Shuang Wu", "Salman Avestimehr", "Tao Zhang"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2022-02-01", "url": "https://arxiv.org/abs/2202.00807", "pdf_url": "https://arxiv.org/pdf/2202.00807v1", "arxiv_id": "2202.00807", "doi": "10.1109/ICASSP43922.2022.9746925", "citation_count": 79, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Acoustics, Speech, and Signal Processing", "quality_score": 0.4758}
{"id": "792681aa7e9d9bd19d1e1bdee4b4e0152c816d0a522340f4c21296d1ff9fd83a", "sources": ["arxiv", "semantic_scholar"], "title": "Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?", "abstract": "In this paper, we question the rationale behind propagating large numbers of parameters through a distributed system during federated learning. We start by examining the rank characteristics of the subspace spanned by gradients across epochs (i.e., the gradient-space) in centralized model training, and observe that this gradient-space often consists of a few leading principal components accounting for an overwhelming majority (95-99%) of the explained variance. Motivated by this, we propose the \"Look-back Gradient Multiplier\" (LBGM) algorithm, which exploits this low-rank property to enable gradient recycling between model update rounds of federated learning, reducing transmissions of large parameters to single scalars for aggregation. We analytically characterize the convergence behavior of LBGM, revealing the nature of the trade-off between communication savings and model performance. Our subsequent experimental results demonstrate the improvement LBGM obtains in communication overhead compared to conventional federated learning on several datasets and deep learning models. Additionally, we show that LBGM is a general plug-and-play algorithm that can be used standalone or stacked on top of existing sparsification techniques for distributed model training.", "authors": ["Sheikh Shams Azam", "Seyyedali Hosseinalipour", "Qiang Qiu", "Christopher Brinton"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2022-02-01", "url": "https://arxiv.org/abs/2202.00280", "pdf_url": "https://arxiv.org/pdf/2202.00280v1", "arxiv_id": "2202.00280", "doi": null, "citation_count": 27, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "International Conference on Learning Representations", "quality_score": 0.3618}
{"id": "ec60d080970e0c789d0a5caf40c57347bad71bc0c14a607f3ba538508adb46dc", "sources": ["arxiv", "semantic_scholar"], "title": "Stochastic Coded Federated Learning with Convergence and Privacy Guarantees", "abstract": "Federated learning (FL) has attracted much attention as a privacy-preserving distributed machine learning framework, where many clients collaboratively train a machine learning model by exchanging model updates with a parameter server instead of sharing their raw data. Nevertheless, FL training suffers from slow convergence and unstable performance due to stragglers caused by the heterogeneous computational resources of clients and fluctuating communication rates. This paper proposes a coded FL framework to mitigate the straggler issue, namely stochastic coded federated learning (SCFL). In this framework, each client generates a privacy-preserving coded dataset by adding additive noise to the random linear combination of its local data. The server collects the coded datasets from all the clients to construct a composite dataset, which helps to compensate for the straggling effect. In the training process, the server as well as clients perform mini-batch stochastic gradient descent (SGD), and the server adds a make-up term in model aggregation to obtain unbiased gradient estimates. We characterize the privacy guarantee by the mutual information differential privacy (MI-DP) and analyze the convergence performance in federated learning. Besides, we demonstrate a privacy-performance tradeoff of the proposed SCFL method by analyzing the influence of the privacy constraint on the convergence rate. Finally, numerical experiments corroborate our analysis and show the benefits of SCFL in achieving fast convergence while preserving data privacy.", "authors": ["Yuchang Sun", "Jiawei Shao", "Songze Li", "Yuyi Mao", "Jun Zhang"], "categories": ["cs.LG", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2022-01-25", "url": "https://arxiv.org/abs/2201.10092", "pdf_url": "https://arxiv.org/pdf/2201.10092v5", "arxiv_id": "2201.10092", "doi": "10.1109/ISIT50566.2022.9834445", "citation_count": 20, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Symposium on Information Theory", "quality_score": 0.3306}
{"id": "ee2aaac74f3e9b1517a2a5e21673ef6eab66dd6e735b48a1d8836ed50bad7a9c", "sources": ["arxiv", "semantic_scholar"], "title": "Building a Privacy-Preserving Smart Camera System", "abstract": "Millions of consumers depend on smart camera systems to remotely monitor their homes and businesses. However, the architecture and design of popular commercial systems require users to relinquish control of their data to untrusted third parties, such as service providers (e.g., the cloud). Third parties therefore can (and in some instances have) access the video footage without the users' knowledge or consent -- violating the core tenet of user privacy. In this paper, we present CaCTUs, a privacy-preserving smart Camera system Controlled Totally by Users. CaCTUs returns control to the user; the root of trust begins with the user and is maintained through a series of cryptographic protocols, designed to support popular features, such as sharing, deleting, and viewing videos live. We show that the system can support live streaming with a latency of 2s at a frame rate of 10fps and a resolution of 480p. In so doing, we demonstrate that it is feasible to implement a performant smart-camera system that leverages the convenience of a cloud-based model while retaining the ability to control access to (private) data.", "authors": ["Yohan Beugin", "Quinn Burke", "Blaine Hoak", "Ryan Sheatsley", "Eric Pauley", "Gang Tan", "Syed Rafiul Hussain", "Patrick McDaniel"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2022-01-23", "url": "https://arxiv.org/abs/2201.09338", "pdf_url": "https://arxiv.org/pdf/2201.09338v1", "arxiv_id": "2201.09338", "doi": "10.2478/popets-2022-0034", "citation_count": 12, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Proceedings on Privacy Enhancing Technologies", "quality_score": 0.2785}
{"id": "baa8435e3867a3639afaa99703fde2c171568d566d00695f59d1c7c3a0c1d946", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Federated Clustering: A Federated Fuzzy $c$-Means Algorithm (FFCM)", "abstract": "Federated Learning (FL) is a setting where multiple parties with distributed data collaborate in training a joint Machine Learning (ML) model while keeping all data local at the parties. Federated clustering is an area of research within FL that is concerned with grouping together data that is globally similar while keeping all data local. We describe how this area of research can be of interest in itself, or how it helps addressing issues like non-independently-identically-distributed (i.i.d.) data in supervised FL frameworks. The focus of this work, however, is an extension of the federated fuzzy $c$-means algorithm to the FL setting (FFCM) as a contribution towards federated clustering. We propose two methods to calculate global cluster centers and evaluate their behaviour through challenging numerical experiments. We observe that one of the methods is able to identify good global clusters even in challenging scenarios, but also acknowledge that many challenges remain open.", "authors": ["Morris Stallmann", "Anna Wilbik"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-01-18", "url": "https://arxiv.org/abs/2201.07316", "pdf_url": "https://arxiv.org/pdf/2201.07316v1", "arxiv_id": "2201.07316", "doi": null, "citation_count": 53, "influential_citation_count": 6, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4331}
{"id": "6f50794142c78d252d70ac7480086ea349787df40a5720bdbfc8fe27d2d1196b", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Maximum Matching on General Graphs and its Application to Enable Privacy-Preserving Kidney Exchange", "abstract": "To this day, there are still some countries where the exchange of kidneys between multiple incompatible patient-donor pairs is restricted by law. Typically, legal regulations in this context are put in place to prohibit coercion and manipulation in order to prevent a market for organ trade. Yet, in countries where kidney exchange is practiced, existing platforms to facilitate such exchanges generally lack sufficient privacy mechanisms. In this paper, we propose a privacy-preserving protocol for kidney exchange that not only addresses the privacy problem of existing platforms but also is geared to lead the way in overcoming legal issues in those countries where kidney exchange is still not practiced. In our approach, we use the concept of secret sharing to distribute the medical data of patients and donors among a set of computing peers in a privacy-preserving fashion. These computing peers then execute our new Secure Multi-Party Computation (SMPC) protocol among each other to determine an optimal set of kidney exchanges. As part of our new protocol, we devise a privacy-preserving solution to the maximum matching problem on general graphs. We have implemented the protocol in the SMPC benchmarking framework MP-SPDZ and provide a comprehensive performance evaluation. Furthermore, we analyze the practicality of our protocol when used in a dynamic setting (where patients and donors arrive and depart over time) based on a data set from the United Network for Organ Sharing.", "authors": ["Malte Breuer", "Ulrike Meyer", "Susanne Wetzel"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2022-01-17", "url": "https://arxiv.org/abs/2201.06446", "pdf_url": "https://arxiv.org/pdf/2201.06446v2", "arxiv_id": "2201.06446", "doi": "10.1145/3508398.3511509", "citation_count": 9, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Conference on Data and Application Security and Privacy", "quality_score": 0.25}
{"id": "91ac3e5cedbf6ef0c78697da1e25a19d36d30d39b5e360097ad9c122856cb133", "sources": ["arxiv", "semantic_scholar"], "title": "Sparsified Secure Aggregation for Privacy-Preserving Federated Learning", "abstract": "Secure aggregation is a popular protocol in privacy-preserving federated learning, which allows model aggregation without revealing the individual models in the clear. On the other hand, conventional secure aggregation protocols incur a significant communication overhead, which can become a major bottleneck in real-world bandwidth-limited applications. Towards addressing this challenge, in this work we propose a lightweight gradient sparsification framework for secure aggregation, in which the server learns the aggregate of the sparsified local model updates from a large number of users, but without learning the individual parameters. Our theoretical analysis demonstrates that the proposed framework can significantly reduce the communication overhead of secure aggregation while ensuring comparable computational complexity. We further identify a trade-off between privacy and communication efficiency due to sparsification. Our experiments demonstrate that our framework reduces the communication overhead by up to 7.8x, while also speeding up the wall clock training time by 1.13x, when compared to conventional secure aggregation benchmarks.", "authors": ["Irem Ergun", "Hasin Us Sami", "Basak Guler"], "categories": ["cs.LG", "cs.CR", "cs.DC", "cs.IT"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2021-12-23", "url": "https://arxiv.org/abs/2112.12872", "pdf_url": "https://arxiv.org/pdf/2112.12872v1", "arxiv_id": "2112.12872", "doi": null, "citation_count": 31, "influential_citation_count": 5, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3891}
{"id": "8b27a8f1850e12d07c0064b3c51053a09f303f1f96dedce87484efe82ab7e2fd", "sources": ["arxiv", "semantic_scholar"], "title": "Distributed Machine Learning and the Semblance of Trust", "abstract": "The utilisation of large and diverse datasets for machine learning (ML) at scale is required to promote scientific insight into many meaningful problems. However, due to data governance regulations such as GDPR as well as ethical concerns, the aggregation of personal and sensitive data is problematic, which prompted the development of alternative strategies such as distributed ML (DML). Techniques such as Federated Learning (FL) allow the data owner to maintain data governance and perform model training locally without having to share their data. FL and related techniques are often described as privacy-preserving. We explain why this term is not appropriate and outline the risks associated with over-reliance on protocols that were not designed with formal definitions of privacy in mind. We further provide recommendations and examples on how such algorithms can be augmented to provide guarantees of governance, security, privacy and verifiability for a general ML audience without prior exposure to formal privacy techniques.", "authors": ["Dmitrii Usynin", "Alexander Ziller", "Daniel Rueckert", "Jonathan Passerat-Palmbach", "Georgios Kaissis"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2021-12-21", "url": "https://arxiv.org/abs/2112.11040", "pdf_url": "https://arxiv.org/pdf/2112.11040v1", "arxiv_id": "2112.11040", "doi": null, "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "d3ae9bf284886b8cad921ecb5bd650a44f6cbf3ac586c3c51f868049acce54c7", "sources": ["arxiv", "semantic_scholar"], "title": "Certified Federated Adversarial Training", "abstract": "In federated learning (FL), robust aggregation schemes have been developed to protect against malicious clients. Many robust aggregation schemes rely on certain numbers of benign clients being present in a quorum of workers. This can be hard to guarantee when clients can join at will, or join based on factors such as idle system status, and connected to power and WiFi. We tackle the scenario of securing FL systems conducting adversarial training when a quorum of workers could be completely malicious. We model an attacker who poisons the model to insert a weakness into the adversarial training such that the model displays apparent adversarial robustness, while the attacker can exploit the inserted weakness to bypass the adversarial training and force the model to misclassify adversarial examples. We use abstract interpretation techniques to detect such stealthy attacks and block the corrupted model updates. We show that this defence can preserve adversarial robustness even against an adaptive attacker.", "authors": ["Giulio Zizzo", "Ambrish Rawat", "Mathieu Sinn", "Sergio Maffeis", "Chris Hankin"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2021-12-20", "url": "https://arxiv.org/abs/2112.10525", "pdf_url": "https://arxiv.org/pdf/2112.10525v1", "arxiv_id": "2112.10525", "doi": null, "citation_count": 13, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2865}
{"id": "5949321441070c37c5f3c6bec6b5f2b2b0e2782d23aaa7c3d637601c7764e85d", "sources": ["arxiv"], "title": "Federated Learning with Superquantile Aggregation for Heterogeneous Data", "abstract": "We present a federated learning framework that is designed to robustly deliver good predictive performance across individual clients with heterogeneous data. The proposed approach hinges upon a superquantile-based learning objective that captures the tail statistics of the error distribution over heterogeneous clients. We present a stochastic training algorithm that interleaves differentially private client filtering with federated averaging steps. We prove finite time convergence guarantees for the algorithm: $O(1/\\sqrt{T})$ in the nonconvex case in $T$ communication rounds and $O(\\exp(-T/κ^{3/2}) + κ/T)$ in the strongly convex case with local condition number $κ$. Experimental results on benchmark datasets for federated learning demonstrate that our approach is competitive with classical ones in terms of average error and outperforms them in terms of tail statistics of the error.", "authors": ["Krishna Pillutla", "Yassine Laguel", "Jérôme Malick", "Zaid Harchaoui"], "categories": ["cs.LG", "math.OC", "stat.ML"], "fields_of_study": [], "published_date": "2021-12-17", "url": "https://arxiv.org/abs/2112.09429", "pdf_url": "https://arxiv.org/pdf/2112.09429v2", "arxiv_id": "2112.09429", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Machine Learning (2023): 1-68", "quality_score": 0.0}
{"id": "6d6f4594564d4ad0d4a1139bc731ea7fbd10c3ca4b0d83da33b78de896ef7398", "sources": ["arxiv", "semantic_scholar"], "title": "Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method", "abstract": "Vertical Federated learning (VFL) is a promising paradigm for predictive analytics, empowering an organization (i.e., task party) to enhance its predictive models through collaborations with multiple data suppliers (i.e., data parties) in a decentralized and privacy-preserving way. Despite the fast-growing interest in VFL, the lack of effective and secure tools for assessing the value of data owned by data parties hinders the application of VFL in business contexts. In response, we propose FedValue, a privacy-preserving, task-specific but model-free data valuation method for VFL, which consists of a data valuation metric and a federated computation method. Specifically, we first introduce a novel data valuation metric, namely MShapley-CMI. The metric evaluates a data party's contribution to a predictive analytics task without the need of executing a machine learning model, making it well-suited for real-world applications of VFL. Next, we develop an innovative federated computation method that calculates the MShapley-CMI value for each data party in a privacy-preserving manner. Extensive experiments conducted on six public datasets validate the efficacy of FedValue for data valuation in the context of VFL. In addition, we illustrate the practical utility of FedValue with a case study involving federated movie recommendations.", "authors": ["Xiao Han", "Leye Wang", "Junjie Wu", "Xiao Fang"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2021-12-15", "url": "https://arxiv.org/abs/2112.08364", "pdf_url": "https://arxiv.org/pdf/2112.08364v3", "arxiv_id": "2112.08364", "doi": null, "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1505}
{"id": "9792c8b26dad7cd9dac5f642f68f61c6c2da286960b75f77582f0c66e76efb8f", "sources": ["arxiv", "semantic_scholar"], "title": "FedRAD: Federated Robust Adaptive Distillation", "abstract": "The robustness of federated learning (FL) is vital for the distributed training of an accurate global model that is shared among large number of clients. The collaborative learning framework by typically aggregating model updates is vulnerable to model poisoning attacks from adversarial clients. Since the shared information between the global server and participants are only limited to model parameters, it is challenging to detect bad model updates. Moreover, real-world datasets are usually heterogeneous and not independent and identically distributed (Non-IID) among participants, which makes the design of such robust FL pipeline more difficult. In this work, we propose a novel robust aggregation method, Federated Robust Adaptive Distillation (FedRAD), to detect adversaries and robustly aggregate local models based on properties of the median statistic, and then performing an adapted version of ensemble Knowledge Distillation. We run extensive experiments to evaluate the proposed method against recently published works. The results show that FedRAD outperforms all other aggregators in the presence of adversaries, as well as in heterogeneous data distributions.", "authors": ["Stefán Páll Sturluson", "Samuel Trew", "Luis Muñoz-González", "Matei Grama", "Jonathan Passerat-Palmbach", "Daniel Rueckert", "Amir Alansary"], "categories": ["cs.LG", "cs.AI", "cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2021-12-02", "url": "https://arxiv.org/abs/2112.01405", "pdf_url": "https://arxiv.org/pdf/2112.01405v1", "arxiv_id": "2112.01405", "doi": null, "citation_count": 25, "influential_citation_count": 5, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3891}
{"id": "acf0880e6c8a1a0a538d392bbc96f59bb8546584a71b2154c47780dd81dedcd4", "sources": ["arxiv", "semantic_scholar"], "title": "FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks", "abstract": "Federated Learning (FL) is a distributed learning paradigm that can learn a global or personalized model from decentralized datasets on edge devices. However, in the computer vision domain, model performance in FL is far behind centralized training due to the lack of exploration in diverse tasks with a unified FL framework. FL has rarely been demonstrated effectively in advanced computer vision tasks such as object detection and image segmentation. To bridge the gap and facilitate the development of FL for computer vision tasks, in this work, we propose a federated learning library and benchmarking framework, named FedCV, to evaluate FL on the three most representative computer vision tasks: image classification, image segmentation, and object detection. We provide non-I.I.D. benchmarking datasets, models, and various reference FL algorithms. Our benchmark study suggests that there are multiple challenges that deserve future exploration: centralized training tricks may not be directly applied to FL; the non-I.I.D. dataset actually downgrades the model accuracy to some degree in different tasks; improving the system efficiency of federated training is challenging given the huge number of parameters and the per-client memory cost. We believe that such a library and benchmark, along with comparable evaluation settings, is necessary to make meaningful progress in FL on computer vision tasks. FedCV is publicly available: https://github.com/FedML-AI/FedCV.", "authors": ["Chaoyang He", "Alay Dilipbhai Shah", "Zhenheng Tang", "Di Fan1Adarshan Naiynar Sivashunmugam", "Keerti Bhogaraju", "Mita Shimpi", "Li Shen", "Xiaowen Chu", "Mahdi Soltanolkotabi", "Salman Avestimehr"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2021-11-22", "url": "https://arxiv.org/abs/2111.11066", "pdf_url": "https://arxiv.org/pdf/2111.11066v1", "arxiv_id": "2111.11066", "doi": null, "citation_count": 82, "influential_citation_count": 4, "has_code": true, "code_url": "https://github.com/FedML-AI/FedCV", "venue": "arXiv.org", "quality_score": 0.4798}
{"id": "489220bd0eb464d46d92246c0ac2dc00b50c910c35cce361b0040becccb94de8", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-preserving Federated Learning for Residential Short Term Load Forecasting", "abstract": "With high levels of intermittent power generation and dynamic demand patterns, accurate forecasts for residential loads have become essential. Smart meters can play an important role when making these forecasts as they provide detailed load data. However, using smart meter data for load forecasting is challenging due to data privacy requirements. This paper investigates how these requirements can be addressed through a combination of federated learning and privacy preserving techniques such as differential privacy and secure aggregation. For our analysis, we employ a large set of residential load data and simulate how different federated learning models and privacy preserving techniques affect performance and privacy. Our simulations reveal that combining federated learning and privacy preserving techniques can secure both high forecasting accuracy and near-complete privacy. Specifically, we find that such combinations enable a high level of information sharing while ensuring privacy of both the processed load data and forecasting models. Moreover, we identify and discuss challenges of applying federated learning, differential privacy and secure aggregation for residential short-term load forecasting.", "authors": ["Joaquin Delgado Fernandez", "Sergio Potenciano Menci", "Charles Lee", "Gilbert Fridgen"], "categories": ["cs.LG", "cs.AI", "eess.SY"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2021-11-17", "url": "https://arxiv.org/abs/2111.09248", "pdf_url": "https://arxiv.org/pdf/2111.09248v4", "arxiv_id": "2111.09248", "doi": "10.1016/j.apenergy.2022.119915", "citation_count": 82, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Applied Energy", "quality_score": 0.4798}
{"id": "128eda9fccc115db91574cf0939aec4857e3a99a3e87e313b832fde2ef1de984", "sources": ["arxiv", "semantic_scholar"], "title": "Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning System", "abstract": "Federated machine learning (FL) allows to collectively train models on sensitive data as only the clients' models and not their training data need to be shared. However, despite the attention that research on FL has drawn, the concept still lacks broad adoption in practice. One of the key reasons is the great challenge to implement FL systems that simultaneously achieve fairness, integrity, and privacy preservation for all participating clients. To contribute to solving this issue, our paper suggests a FL system that incorporates blockchain technology, local differential privacy, and zero-knowledge proofs. Our implementation of a proof-of-concept with multiple linear regression illustrates that these state-of-the-art technologies can be combined to a FL system that aligns economic incentives, trust, and confidentiality requirements in a scalable and transparent system.", "authors": ["Timon Rückel", "Johannes Sedlmeir", "Peter Hofmann"], "categories": ["cs.CR", "cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2021-11-11", "url": "https://arxiv.org/abs/2111.06290", "pdf_url": "https://arxiv.org/pdf/2111.06290v1", "arxiv_id": "2111.06290", "doi": "10.1016/j.comnet.2021.108621", "citation_count": 70, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.4628}
{"id": "f77b5ec44a35da02726282303b0e41d893a1ddc644ce365fbbd0e016a68382f9", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Energy Storage Sharing with Blockchain and Secure Multi-Party Computation", "abstract": "Energy storage provides an effective way of shifting temporal energy demands and supplies, which enables significant cost reduction under time-of-use energy pricing plans. Despite its promising benefits, the cost of present energy storage remains expensive, presenting a major obstacle to practical deployment. A more viable solution to improve the cost-effectiveness is by sharing energy storage, such as community sharing, cloud energy storage and peer-to-peer sharing. However, revealing private energy demand data to an external energy storage operator may compromise user privacy, and is susceptible to data misuses and breaches. In this paper, we explore a novel approach to support energy storage sharing with privacy protection, based on privacy-preserving blockchain and secure multi-party computation. We present an integrated solution to enable privacy-preserving energy storage sharing, such that energy storage service scheduling and cost-sharing can be attained without the knowledge of individual users' demands. It also supports auditing and verification by the grid operator via blockchain. Furthermore, our privacy-preserving solution can safeguard against a dishonest majority of users, who may collude in cheating, without requiring a trusted third-party. We implemented our solution as a smart contract on real-world Ethereum blockchain platform, and provide empirical evaluation in this paper.", "authors": ["Nan Wang", "Sid Chi-Kin Chau", "Yue Zhou"], "categories": ["cs.CR", "math.OC"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2021-11-03", "url": "https://arxiv.org/abs/2111.02005", "pdf_url": "https://arxiv.org/pdf/2111.02005v1", "arxiv_id": "2111.02005", "doi": "10.1145/3447555.3464869", "citation_count": 36, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "Energy-Efficient Computing and Networking", "quality_score": 0.3921}
{"id": "02faf51cb77e308e5f05912ceb2ed897e3d3232629a30291ede185cb2b2611c1", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning with Heterogeneous Differential Privacy", "abstract": "Federated learning (FL) takes a first step towards privacy-preserving machine learning by training models while keeping client data local. Models trained using FL may still leak private client information through model updates during training. Differential privacy (DP) may be employed on model updates to provide privacy guarantees within FL, typically at the cost of degraded performance of the final trained model. Both non-private FL and DP-FL can be solved using variants of the federated averaging (FedAvg) algorithm. In this work, we consider a heterogeneous DP setup where clients require varying degrees of privacy guarantees. First, we analyze the optimal solution to the federated linear regression problem with heterogeneous DP in a Bayesian setup. We find that unlike the non-private setup, where the optimal solution for homogeneous data amounts to a single global solution for all clients learned through FedAvg, the optimal solution for each client in this setup would be a personalized one even for homogeneous data. We also analyze the privacy-utility trade-off for this setup, where we characterize the gain obtained from heterogeneous privacy where some clients opt for less strict privacy guarantees. We propose a new algorithm for FL with heterogeneous DP, named FedHDP, which employs personalization and weighted averaging at the server using the privacy choices of clients, to achieve better performance on clients' local models. Through numerical experiments, we show that FedHDP provides up to $9.27\\%$ performance gain compared to the baseline DP-FL for the considered datasets where $5\\%$ of clients opt out of DP. Additionally, we show a gap in the average performance of local models between non-private and private clients of up to $3.49\\%$, empirically illustrating that the baseline DP-FL might incur a large utility cost when not all clients require the stricter privacy guarantees.", "authors": ["Nasser Aldaghri", "Hessam Mahdavifar", "Ahmad Beirami"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2021-10-28", "url": "https://arxiv.org/abs/2110.15252", "pdf_url": "https://arxiv.org/pdf/2110.15252v2", "arxiv_id": "2110.15252", "doi": null, "citation_count": 5, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1945}
{"id": "68327b061c9f14ecc60b41c8eaef41eec78e11e128e899ed9774333f781ef09a", "sources": ["arxiv", "semantic_scholar"], "title": "Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching", "abstract": "Mobile edge computing (MEC) is a prominent computing paradigm which expands the application fields of wireless communication. Due to the limitation of the capacities of user equipments and MEC servers, edge caching (EC) optimization is crucial to the effective utilization of the caching resources in MEC-enabled wireless networks. However, the dynamics and complexities of content popularities over space and time as well as the privacy preservation of users pose significant challenges to EC optimization. In this paper, a privacy-preserving distributed deep deterministic policy gradient (P2D3PG) algorithm is proposed to maximize the cache hit rates of devices in the MEC networks. Specifically, we consider the fact that content popularities are dynamic, complicated and unobservable, and formulate the maximization of cache hit rates on devices as distributed problems under the constraints of privacy preservation. In particular, we convert the distributed optimizations into distributed model-free Markov decision process problems and then introduce a privacy-preserving federated learning method for popularity prediction. Subsequently, a P2D3PG algorithm is developed based on distributed reinforcement learning to solve the distributed problems. Simulation results demonstrate the superiority of the proposed approach in improving EC hit rate over the baseline methods while preserving user privacy.", "authors": ["Shengheng Liu", "Chong Zheng", "Yongming Huang", "Tony Q. S. Quek"], "categories": ["cs.LG", "cs.AI", "cs.CR", "cs.MM"], "fields_of_study": ["Computer Science"], "published_date": "2021-10-20", "url": "https://arxiv.org/abs/2110.10349", "pdf_url": "https://arxiv.org/pdf/2110.10349v2", "arxiv_id": "2110.10349", "doi": "10.1109/JSAC.2022.3142348", "citation_count": 66, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "IEEE Journal on Selected Areas in Communications", "quality_score": 0.4565}
{"id": "99d40d2f2d917d8b90b4f59f269080887c6c3c87ac911650205bd03df331d2b0", "sources": ["arxiv", "semantic_scholar"], "title": "Distribution-Free Federated Learning with Conformal Predictions", "abstract": "Federated learning has attracted considerable interest for collaborative machine learning in healthcare to leverage separate institutional datasets while maintaining patient privacy. However, additional challenges such as poor calibration and lack of interpretability may also hamper widespread deployment of federated models into clinical practice, leading to user distrust or misuse of ML tools in high-stakes clinical decision-making. In this paper, we propose to address these challenges by incorporating an adaptive conformal framework into federated learning to ensure distribution-free prediction sets that provide coverage guarantees. Importantly, these uncertainty estimates can be obtained without requiring any additional modifications to the model. Empirical results on the MedMNIST medical imaging benchmark demonstrate our federated method provides tighter coverage over local conformal predictions on 6 different medical imaging datasets for 2D and 3D multi-class classification tasks. Furthermore, we correlate class entropy with prediction set size to assess task uncertainty.", "authors": ["Charles Lu", "Jayasheree Kalpathy-Cramer"], "categories": ["cs.LG", "eess.IV"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2021-10-14", "url": "https://arxiv.org/abs/2110.07661", "pdf_url": "https://arxiv.org/pdf/2110.07661v2", "arxiv_id": "2110.07661", "doi": null, "citation_count": 20, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3306}
{"id": "9890ebe3f379e120b6d09833417758a015484a6984e0f983522f438e2ada163c", "sources": ["arxiv", "semantic_scholar"], "title": "WAFFLE: Weighted Averaging for Personalized Federated Learning", "abstract": "In federated learning, model personalization can be a very effective strategy to deal with heterogeneous training data across clients. We introduce WAFFLE (Weighted Averaging For Federated LEarning), a personalized collaborative machine learning algorithm that leverages stochastic control variates for faster convergence. WAFFLE uses the Euclidean distance between clients' updates to weigh their individual contributions and thus minimize the personalized model loss on the specific agent of interest. Through a series of experiments, we compare our new approach to two recent personalized federated learning methods--Weight Erosion and APFL--as well as two general FL methods--Federated Averaging and SCAFFOLD. Performance is evaluated using two categories of non-identical client data distributions--concept shift and label skew--on two image data sets (MNIST and CIFAR10). Our experiments demonstrate the comparative effectiveness of WAFFLE, as it achieves or improves accuracy with faster convergence.", "authors": ["Martin Beaussart", "Felix Grimberg", "Mary-Anne Hartley", "Martin Jaggi"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2021-10-13", "url": "https://arxiv.org/abs/2110.06978", "pdf_url": "https://arxiv.org/pdf/2110.06978v2", "arxiv_id": "2110.06978", "doi": null, "citation_count": 20, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3306}
{"id": "bae9ee0f2fa70ab8eb6fc4d7c66b820cc91309f31018ae119e236cd7230ae01f", "sources": ["arxiv", "semantic_scholar"], "title": "Complex-valued Federated Learning with Differential Privacy and MRI Applications", "abstract": "Federated learning enhanced with Differential Privacy (DP) is a powerful privacy-preserving strategy to protect individuals sharing their sensitive data for processing in fields such as medicine and healthcare. Many medical applications, for example magnetic resonance imaging (MRI), rely on complex-valued signal processing techniques for data acquisition and analysis. However, the appropriate application of DP to complex-valued data is still underexplored. To address this issue, from the theoretical side, we introduce the complex-valued Gaussian mechanism, whose behaviour we characterise in terms of $f$-DP, $(\\varepsilon, δ)$-DP and Rényi-DP. Moreover, we generalise the fundamental algorithm DP stochastic gradient descent to complex-valued neural networks and present novel complex-valued neural network primitives compatible with DP. Experimentally, we showcase a proof-of-concept by training federated complex-valued neural networks with DP on a real-world task (MRI pulse sequence classification in $k$-space), yielding excellent utility and privacy. Our results highlight the relevance of combining federated learning with robust privacy-preserving techniques in the MRI context.", "authors": ["Anneliese Riess", "Alexander Ziller", "Stefan Kolek", "Daniel Rueckert", "Julia Schnabel", "Georgios Kaissis"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2021-10-07", "url": "https://arxiv.org/abs/2110.03478", "pdf_url": "https://arxiv.org/pdf/2110.03478v2", "arxiv_id": "2110.03478", "doi": "10.1007/978-3-031-77610-6_18", "citation_count": 7, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2258}
{"id": "48482688f22e62211450c5720d45d325e876500c7a1f14b2b2925b2518f84181", "sources": ["arxiv", "semantic_scholar"], "title": "The Connection between Out-of-Distribution Generalization and Privacy of ML Models", "abstract": "With the goal of generalizing to out-of-distribution (OOD) data, recent domain generalization methods aim to learn \"stable\" feature representations whose effect on the output remains invariant across domains. Given the theoretical connection between generalization and privacy, we ask whether better OOD generalization leads to better privacy for machine learning models, where privacy is measured through robustness to membership inference (MI) attacks. In general, we find that the relationship does not hold. Through extensive evaluation on a synthetic dataset and image datasets like MNIST, Fashion-MNIST, and Chest X-rays, we show that a lower OOD generalization gap does not imply better robustness to MI attacks. Instead, privacy benefits are based on the extent to which a model captures the stable features. A model that captures stable features is more robust to MI attacks than models that exhibit better OOD generalization but do not learn stable features. Further, for the same provable differential privacy guarantees, a model that learns stable features provides higher utility as compared to others. Our results offer the first extensive empirical study connecting stable features and privacy, and also have a takeaway for the domain generalization community; MI attack can be used as a complementary metric to measure model quality.", "authors": ["Divyat Mahajan", "Shruti Tople", "Amit Sharma"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2021-10-07", "url": "https://arxiv.org/abs/2110.03369", "pdf_url": "https://arxiv.org/pdf/2110.03369v1", "arxiv_id": "2110.03369", "doi": null, "citation_count": 7, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/microsoft/robustdg", "venue": "arXiv.org", "quality_score": 0.2258}
{"id": "f384ea3ea5991bb61afb2d45668e03ec5fa1943ce1d44e1368264a89a57ba95e", "sources": ["arxiv", "semantic_scholar"], "title": "3LegRace: Privacy-Preserving DNN Training over TEEs and GPUs", "abstract": "Leveraging parallel hardware (e.g. GPUs) for deep neural network (DNN) training brings high computing performance. However, it raises data privacy concerns as GPUs lack a trusted environment to protect the data. Trusted execution environments (TEEs) have emerged as a promising solution to achieve privacy-preserving learning. Unfortunately, TEEs' limited computing power renders them not comparable to GPUs in performance. To improve the trade-off among privacy, computing performance, and model accuracy, we propose an \\emph{asymmetric} model decomposition framework, \\AsymML{}, to (1) accelerate training using parallel hardware; and (2) achieve a strong privacy guarantee using TEEs and differential privacy (DP) with much less accuracy compromised compared to DP-only methods. By exploiting the low-rank characteristics in training data and intermediate features, \\AsymML{} asymmetrically decomposes inputs and intermediate activations into low-rank and residual parts. With the decomposed data, the target DNN model is accordingly split into a \\emph{trusted} and an \\emph{untrusted} part. The trusted part performs computations on low-rank data, with low compute and memory costs. The untrusted part is fed with residuals perturbed by very small noise. Privacy, computing performance, and model accuracy are well managed by respectively delegating the trusted and the untrusted part to TEEs and GPUs. We provide a formal DP guarantee that demonstrates that, for the same privacy guarantee, combining asymmetric data decomposition and DP requires much smaller noise compared to solely using DP without decomposition. This improves the privacy-utility trade-off significantly compared to using only DP methods without decomposition. Furthermore, we present a rank bound analysis showing that the low-rank structure is preserved after each layer across the entire model.", "authors": ["Yue Niu", "Ramy E. Ali", "Salman Avestimehr"], "categories": ["cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2021-10-04", "url": "https://arxiv.org/abs/2110.01229", "pdf_url": "https://arxiv.org/pdf/2110.01229v2", "arxiv_id": "2110.01229", "doi": "10.56553/popets-2022-0105", "citation_count": 23, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Proceedings on Privacy Enhancing Technologies", "quality_score": 0.3451}
{"id": "f5b120ec533d8126173fbdb8378bfd6f170542cb8d6f7ec91db3104076e1fcd4", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning", "abstract": "This paper investigates the feasibility of federated representation learning under the constraints of communication cost and privacy protection. Existing works either conduct annotation-guided local training which requires frequent communication or aggregates the client models via weight averaging which has potential risks of privacy exposure. To tackle the above problems, we first identify that self-supervised contrastive local training is robust against the non-identically distributed data, which provides the feasibility of longer local training and thus reduces the communication cost. Then based on the aforementioned robustness, we propose a novel Federated representation Learning framework with Ensemble Similarity Distillation~(FLESD) that utilizes this robustness. At each round of communication, the server first gathers a fraction of the clients' inferred similarity matrices on a public dataset. Then it ensembles the similarity matrices and train the global model via similarity distillation. We verify the effectiveness of FLESD by a series of empirical experiments and show that, despite stricter constraints, it achieves comparable results under multiple settings on multiple datasets.", "authors": ["Haizhou Shi", "Youcai Zhang", "Zijin Shen", "Siliang Tang", "Yaqian Li", "Yandong Guo", "Yueting Zhuang"], "categories": ["cs.LG", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2021-09-29", "url": "https://arxiv.org/abs/2109.14611", "pdf_url": "https://arxiv.org/pdf/2109.14611v2", "arxiv_id": "2109.14611", "doi": null, "citation_count": 8, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2386}
{"id": "7773d888a8c5eef7b050b788c020404e536297d8da2d1e50b47398dfecf10d4f", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Deep Learning with Bayesian Privacy", "abstract": "Federated learning (FL) aims to protect data privacy by cooperatively learning a model without sharing private data among users. For Federated Learning of Deep Neural Network with billions of model parameters, existing privacy-preserving solutions are unsatisfactory. Homomorphic encryption (HE) based methods provide secure privacy protections but suffer from extremely high computational and communication overheads rendering it almost useless in practice . Deep learning with Differential Privacy (DP) was implemented as a practical learning algorithm at a manageable cost in complexity. However, DP is vulnerable to aggressive Bayesian restoration attacks as disclosed in the literature and demonstrated in experimental results of this work. To address the aforementioned perplexity, we propose a novel Bayesian Privacy (BP) framework which enables Bayesian restoration attacks to be formulated as the probability of reconstructing private data from observed public information. Specifically, the proposed BP framework accurately quantifies privacy loss by Kullback-Leibler (KL) Divergence between the prior distribution about the privacy data and the posterior distribution of restoration private data conditioning on exposed information}. To our best knowledge, this Bayesian Privacy analysis is the first to provides theoretical justification of secure privacy-preserving capabilities against Bayesian restoration attacks. As a concrete use case, we demonstrate that a novel federated deep learning method using private passport layers is able to simultaneously achieve high model performance, privacy-preserving capability and low computational complexity. Theoretical analysis is in accordance with empirical measurements of information leakage extensively experimented with a variety of DNN networks on image classification MNIST, CIFAR10, and CIFAR100 datasets.", "authors": ["Hanlin Gu", "Lixin Fan", "Bowen Li", "Yan Kang", "Yuan Yao", "Qiang Yang"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2021-09-27", "url": "https://arxiv.org/abs/2109.13012", "pdf_url": "https://arxiv.org/pdf/2109.13012v1", "arxiv_id": "2109.13012", "doi": null, "citation_count": 24, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3495}
{"id": "4f7d1fb36b29738305638e06c89668954b79f682508d9846f7cc847af1669025", "sources": ["arxiv", "semantic_scholar"], "title": "Training Fair Models in Federated Learning without Data Privacy Infringement", "abstract": "Training fair machine learning models becomes more and more important. As many powerful models are trained by collaboration among multiple parties, each holding some sensitive data, it is natural to explore the feasibility of training fair models in federated learning so that the fairness of trained models, the data privacy of clients, and the collaboration between clients can be fully respected simultaneously. However, the task of training fair models in federated learning is challenging, since it is far from trivial to estimate the fairness of a model without knowing the private data of the participating parties, which is often constrained by privacy requirements in federated learning. In this paper, we first propose a federated estimation method to accurately estimate the fairness of a model without infringing the data privacy of any party. Then, we use the fairness estimation to formulate a novel problem of training fair models in federated learning. We develop FedFair, a well-designed federated learning framework, which can successfully train a fair model with high performance without data privacy infringement. Our extensive experiments on three real-world data sets demonstrate the excellent fair model training performance of our method.", "authors": ["Xin Che", "Jingdi Hu", "Zirui Zhou", "Yong Zhang", "Lingyang Chu"], "categories": ["cs.LG", "cs.CY"], "fields_of_study": ["Computer Science"], "published_date": "2021-09-13", "url": "https://arxiv.org/abs/2109.05662", "pdf_url": "https://arxiv.org/pdf/2109.05662v2", "arxiv_id": "2109.05662", "doi": "10.1109/BigData62323.2024.10825911", "citation_count": 39, "influential_citation_count": 5, "has_code": false, "code_url": null, "venue": "BigData Congress [Services Society]", "quality_score": 0.4005}
{"id": "41dd7009e9d3fa8adf1b0b8ae94c57c5b6acff8cad5ffe1cae61966fbfb8f65c", "sources": ["arxiv", "semantic_scholar"], "title": "AMI-FML: A Privacy-Preserving Federated Machine Learning Framework for AMI", "abstract": "Machine learning (ML) based smart meter data analytics is very promising for energy management and demand-response applications in the advanced metering infrastructure(AMI). A key challenge in developing distributed ML applications for AMI is to preserve user privacy while allowing active end-users participation. This paper addresses this challenge and proposes a privacy-preserving federated learning framework for ML applications in the AMI. We consider each smart meter as a federated edge device hosting an ML application that exchanges information with a central aggregator or a data concentrator, periodically. Instead of transferring the raw data sensed by the smart meters, the ML model weights are transferred to the aggregator to preserve privacy. The aggregator processes these parameters to devise a robust ML model that can be substituted at each edge device. We also discuss strategies to enhance privacy and improve communication efficiency while sharing the ML model parameters, suited for relatively slow network connections in the AMI. We demonstrate the proposed framework on a use case federated ML (FML) application that improves short-term load forecasting (STLF). We use a long short-term memory(LSTM) recurrent neural network (RNN) model for STLF. In our architecture, we assume that there is an aggregator connected to a group of smart meters. The aggregator uses the learned model gradients received from the federated smart meters to generate an aggregate, robust RNN model which improves the forecasting accuracy for individual and aggregated STLF. Our results indicate that with FML, forecasting accuracy is increased while preserving the data privacy of the end-users.", "authors": ["Milan Biswal", "Abu Saleh Md Tayeen", "Satyajayant Misra"], "categories": ["cs.LG", "eess.SY"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2021-09-13", "url": "https://arxiv.org/abs/2109.05666", "pdf_url": "https://arxiv.org/pdf/2109.05666v2", "arxiv_id": "2109.05666", "doi": null, "citation_count": 14, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.294}
{"id": "989d50ed9e0d7bd16960c42632f8f4efae7567dadf4704a5250a95cac8bf6279", "sources": ["arxiv", "semantic_scholar"], "title": "GRP-FED: Addressing Client Imbalance in Federated Learning via Global-Regularized Personalization", "abstract": "Since data is presented long-tailed in reality, it is challenging for Federated Learning (FL) to train across decentralized clients as practical applications. We present Global-Regularized Personalization (GRP-FED) to tackle the data imbalanced issue by considering a single global model and multiple local models for each client. With adaptive aggregation, the global model treats multiple clients fairly and mitigates the global long-tailed issue. Each local model is learned from the local data and aligns with its distribution for customization. To prevent the local model from just overfitting, GRP-FED applies an adversarial discriminator to regularize between the learned global-local features. Extensive results show that our GRP-FED improves under both global and local scenarios on real-world MIT-BIH and synthesis CIFAR-10 datasets, achieving comparable performance and addressing client imbalance.", "authors": ["Yen-Hsiu Chou", "Shenda Hong", "Chenxi Sun", "Derun Cai", "Moxian Song", "Hongyan Li"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2021-08-31", "url": "https://arxiv.org/abs/2108.13858", "pdf_url": "https://arxiv.org/pdf/2108.13858v1", "arxiv_id": "2108.13858", "doi": "10.1137/1.9781611977172.51", "citation_count": 17, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "SDM", "quality_score": 0.3138}
{"id": "0c8893d113e92dd65d6586e27d3e66d12093c6771f8123359bc4672c3e4b5ac2", "sources": ["arxiv", "semantic_scholar"], "title": "Private Multi-Task Learning: Formulation and Applications to Federated Learning", "abstract": "Many problems in machine learning rely on multi-task learning (MTL), in which the goal is to solve multiple related machine learning tasks simultaneously. MTL is particularly relevant for privacy-sensitive applications in areas such as healthcare, finance, and IoT computing, where sensitive data from multiple, varied sources are shared for the purpose of learning. In this work, we formalize notions of client-level privacy for MTL via joint differential privacy (JDP), a relaxation of differential privacy for mechanism design and distributed optimization. We then propose an algorithm for mean-regularized MTL, an objective commonly used for applications in personalized federated learning, subject to JDP. We analyze our objective and solver, providing certifiable guarantees on both privacy and utility. Empirically, we find that our method provides improved privacy/utility trade-offs relative to global baselines across common federated learning benchmarks.", "authors": ["Shengyuan Hu", "Zhiwei Steven Wu", "Virginia Smith"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2021-08-30", "url": "https://arxiv.org/abs/2108.12978", "pdf_url": "https://arxiv.org/pdf/2108.12978v3", "arxiv_id": "2108.12978", "doi": null, "citation_count": 23, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3451}
{"id": "6cb063bd03dc17979a45f14866528717926dff3850496e968c23b5eacb7f755c", "sources": ["arxiv", "semantic_scholar"], "title": "PIVODL: Privacy-preserving vertical federated learning over distributed labels", "abstract": "Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data. Non-parametric models like gradient boosting decision trees (GBDT) have been commonly used in FL for vertically partitioned data. However, all these studies assume that all the data labels are stored on only one client, which may be unrealistic for real-world applications. Therefore, in this work, we propose a secure vertical FL framework, named PIVODL, to train GBDT with data labels distributed on multiple devices. Both homomorphic encryption and differential privacy are adopted to prevent label information from being leaked through transmitted gradients and leaf values. Our experimental results show that both information leakage and model performance degradation of the proposed PIVODL are negligible.", "authors": ["Hangyu Zhu", "Rui Wang", "Yaochu Jin", "Kaitai Liang"], "categories": ["cs.CR", "cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2021-08-25", "url": "https://arxiv.org/abs/2108.11444", "pdf_url": "https://arxiv.org/pdf/2108.11444v1", "arxiv_id": "2108.11444", "doi": "10.1109/TAI.2021.3139055", "citation_count": 37, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Artificial Intelligence", "quality_score": 0.3949}
{"id": "491a0ba571a687743b22f56518196b956353a22ee4e2441d7842458a7013b70e", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health", "abstract": "Privacy protection is an ethical issue with broad concern in Artificial Intelligence (AI). Federated learning is a new machine learning paradigm to learn a shared model across users or organisations without direct access to the data. It has great potential to be the next-general AI model training framework that offers privacy protection and therefore has broad implications for the future of digital health and healthcare informatics. Implementing an open innovation framework in the healthcare industry, namely open health, is to enhance innovation and creative capability of health-related organisations by building a next-generation collaborative framework with partner organisations and the research community. In particular, this game-changing collaborative framework offers knowledge sharing from diverse data with a privacy-preserving. This chapter will discuss how federated learning can enable the development of an open health ecosystem with the support of AI. Existing challenges and solutions for federated learning will be discussed.", "authors": ["Guodong Long", "Tao Shen", "Yue Tan", "Leah Gerrard", "Allison Clarke", "Jing Jiang"], "categories": ["cs.DC", "cs.AI", "cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2021-08-24", "url": "https://arxiv.org/abs/2108.10761", "pdf_url": "https://arxiv.org/pdf/2108.10761v1", "arxiv_id": "2108.10761", "doi": "10.1007/978-3-030-72188-6_6", "citation_count": 60, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.4463}
{"id": "b5a79b1431492bc5bd0735f3fbbc73835ed335495316f29ae23289c897bc0cb5", "sources": ["arxiv", "semantic_scholar"], "title": "Aegis: A Trusted, Automatic and Accurate Verification Framework for Vertical Federated Learning", "abstract": "Vertical federated learning (VFL) leverages various privacy-preserving algorithms, e.g., homomorphic encryption or secret sharing based SecureBoost, to ensure data privacy. However, these algorithms all require a semi-honest secure definition, which raises concerns in real-world applications. In this paper, we present Aegis, a trusted, automatic, and accurate verification framework to verify the security of VFL jobs. Aegis is separated from local parties to ensure the security of the framework. Furthermore, it automatically adapts to evolving VFL algorithms by defining the VFL job as a finite state machine to uniformly verify different algorithms and reproduce the entire job to provide more accurate verification. We implement and evaluate Aegis with different threat models on financial and medical datasets. Evaluation results show that: 1) Aegis can detect 95% threat models, and 2) it provides fine-grained verification results within 84% of the total VFL job time.", "authors": ["Cengguang Zhang", "Junxue Zhang", "Di Chai", "Kai Chen"], "categories": ["cs.CR", "cs.AI", "cs.DC", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2021-08-16", "url": "https://arxiv.org/abs/2108.06958", "pdf_url": "https://arxiv.org/pdf/2108.06958v2", "arxiv_id": "2108.06958", "doi": null, "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2113}
{"id": "a8a0cc08f015cbc2ac2acf6325889d38c2639646ce06955e2173a52c5fe1d7f5", "sources": ["arxiv", "semantic_scholar"], "title": "Dynamic Attention-based Communication-Efficient Federated Learning", "abstract": "Federated learning (FL) offers a solution to train a global machine learning model while still maintaining data privacy, without needing access to data stored locally at the clients. However, FL suffers performance degradation when client data distribution is non-IID, and a longer training duration to combat this degradation may not necessarily be feasible due to communication limitations. To address this challenge, we propose a new adaptive training algorithm $\\texttt{AdaFL}$, which comprises two components: (i) an attention-based client selection mechanism for a fairer training scheme among the clients; and (ii) a dynamic fraction method to balance the trade-off between performance stability and communication efficiency. Experimental results show that our $\\texttt{AdaFL}$ algorithm outperforms the usual $\\texttt{FedAvg}$ algorithm, and can be incorporated to further improve various state-of-the-art FL algorithms, with respect to three aspects: model accuracy, performance stability, and communication efficiency.", "authors": ["Zihan Chen", "Kai Fong Ernest Chong", "Tony Q. S. Quek"], "categories": ["cs.LG", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2021-08-12", "url": "https://arxiv.org/abs/2108.05765", "pdf_url": "https://arxiv.org/pdf/2108.05765v1", "arxiv_id": "2108.05765", "doi": null, "citation_count": 15, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.301}
{"id": "81f07ac09e703b0ebf28c2d4a49cf0554c82bf36eb71415e1e3583c473b0cc49", "sources": ["arxiv", "semantic_scholar"], "title": "A Contract Theory based Incentive Mechanism for Federated Learning", "abstract": "Federated learning (FL) serves as a data privacy-preserved machine learning paradigm, and realizes the collaborative model trained by distributed clients. To accomplish an FL task, the task publisher needs to pay financial incentives to the FL server and FL server offloads the task to the contributing FL clients. It is challenging to design proper incentives for the FL clients due to the fact that the task is privately trained by the clients. This paper aims to propose a contract theory based FL task training model towards minimizing incentive budget subject to clients being individually rational (IR) and incentive compatible (IC) in each FL training round. We design a two-dimensional contract model by formally defining two private types of clients, namely data quality and computation effort. To effectively aggregate the trained models, a contract-based aggregator is proposed. We analyze the feasible and optimal contract solutions to the proposed contract model. %Experimental results demonstrate that the proposed framework and contract model can effective improve the generation accuracy of FL tasks. Experimental results show that the generalization accuracy of the FL tasks can be improved by the proposed incentive mechanism where contract-based aggregation is applied.", "authors": ["Mengmeng Tian", "Yuxin Chen", "Yuan Liu", "Zehui Xiong", "Cyril Leung", "Chunyan Miao"], "categories": ["cs.DC", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2021-08-12", "url": "https://arxiv.org/abs/2108.05568", "pdf_url": "https://arxiv.org/pdf/2108.05568v1", "arxiv_id": "2108.05568", "doi": null, "citation_count": 38, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3978}
{"id": "f6304138e1697c2da5706de8e8a7ac2469cd5b5c71bc90049d12e3f2b4d5f284", "sources": ["arxiv", "semantic_scholar"], "title": "Communication Optimization in Large Scale Federated Learning using Autoencoder Compressed Weight Updates", "abstract": "Federated Learning (FL) solves many of this decade's concerns regarding data privacy and computation challenges. FL ensures no data leaves its source as the model is trained at where the data resides. However, FL comes with its own set of challenges. The communication of model weight updates in this distributed environment comes with significant network bandwidth costs. In this context, we propose a mechanism of compressing the weight updates using Autoencoders (AE), which learn the data features of the weight updates and subsequently perform compression. The encoder is set up on each of the nodes where the training is performed while the decoder is set up on the node where the weights are aggregated. This setup achieves compression through the encoder and recreates the weights at the end of every communication round using the decoder. This paper shows that the dynamic and orthogonal AE based weight compression technique could serve as an advantageous alternative (or an add-on) in a large scale FL, as it not only achieves compression ratios ranging from 500x to 1720x and beyond, but can also be modified based on the accuracy requirements, computational capacity, and other requirements of the given FL setup.", "authors": ["Srikanth Chandar", "Pravin Chandran", "Raghavendra Bhat", "Avinash Chakravarthi"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2021-08-12", "url": "https://arxiv.org/abs/2108.05670", "pdf_url": "https://arxiv.org/pdf/2108.05670v1", "arxiv_id": "2108.05670", "doi": null, "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2113}
{"id": "6eead7ac80f115026f4c41470d31f3d7d621dd14ef910935a0be0e63446f98d4", "sources": ["arxiv", "semantic_scholar"], "title": "FederatedNILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring based on Federated Deep Learning", "abstract": "Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household-level into appliance-level consumptions, can help to analyze electricity consumption behaviours of users and enable practical smart energy and smart grid applications. However, smart meters are privately owned and distributed, which make real-world applications of NILM challenging. To this end, this paper develops a distributed and privacy-preserving federated deep learning framework for NILM (FederatedNILM), which combines federated learning with a state-of-the-art deep learning architecture to conduct NILM for the classification of typical states of household appliances. Through extensive comparative experiments, the effectiveness of the proposed FederatedNILM framework is demonstrated.", "authors": ["Shuang Dai", "Fanlin Meng", "Qian Wang", "Xizhong Chen"], "categories": ["cs.LG", "cs.AI", "eess.SP"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2021-08-08", "url": "https://arxiv.org/abs/2108.03591", "pdf_url": "https://arxiv.org/pdf/2108.03591v1", "arxiv_id": "2108.03591", "doi": "10.1109/IJCNN54540.2023.10191549", "citation_count": 25, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Joint Conference on Neural Network", "quality_score": 0.3537}
{"id": "e1ccf5aa6d21e1a7e6c424ec63889f1dd5dfbb4e3735e55e8a971e0610bd1425", "sources": ["arxiv", "semantic_scholar"], "title": "Secure and Privacy-Preserving Federated Learning via Co-Utility", "abstract": "The decentralized nature of federated learning, that often leverages the power of edge devices, makes it vulnerable to attacks against privacy and security. The privacy risk for a peer is that the model update she computes on her private data may, when sent to the model manager, leak information on those private data. Even more obvious are security attacks, whereby one or several malicious peers return wrong model updates in order to disrupt the learning process and lead to a wrong model being learned. In this paper we build a federated learning framework that offers privacy to the participating peers as well as security against Byzantine and poisoning attacks. Our framework consists of several protocols that provide strong privacy to the participating peers via unlinkable anonymity and that are rationally sustainable based on the co-utility property. In other words, no rational party is interested in deviating from the proposed protocols. We leverage the notion of co-utility to build a decentralized co-utile reputation management system that provides incentives for parties to adhere to the protocols. Unlike privacy protection via differential privacy, our approach preserves the values of model updates and hence the accuracy of plain federated learning; unlike privacy protection via update aggregation, our approach preserves the ability to detect bad model updates while substantially reducing the computational overhead compared to methods based on homomorphic encryption.", "authors": ["Josep Domingo-Ferrer", "Alberto Blanco-Justicia", "Jesús Manjón", "David Sánchez"], "categories": ["cs.CR", "cs.DC", "cs.GT", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2021-08-04", "url": "https://arxiv.org/abs/2108.01913", "pdf_url": "https://arxiv.org/pdf/2108.01913v1", "arxiv_id": "2108.01913", "doi": "10.1109/jiot.2021.3102155", "citation_count": 46, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "IEEE Internet of Things Journal", "quality_score": 0.418}
{"id": "5d281791fc8bf2a2b6521ac1fe2dca1773da4099453e6788f491c58e63f84fcd", "sources": ["arxiv", "semantic_scholar"], "title": "Defending against Reconstruction Attack in Vertical Federated Learning", "abstract": "Recently researchers have studied input leakage problems in Federated Learning (FL) where a malicious party can reconstruct sensitive training inputs provided by users from shared gradient. It raises concerns about FL since input leakage contradicts the privacy-preserving intention of using FL. Despite a relatively rich literature on attacks and defenses of input reconstruction in Horizontal FL, input leakage and protection in vertical FL starts to draw researcher's attention recently. In this paper, we study how to defend against input leakage attacks in Vertical FL. We design an adversarial training-based framework that contains three modules: adversarial reconstruction, noise regularization, and distance correlation minimization. Those modules can not only be employed individually but also applied together since they are independent to each other. Through extensive experiments on a large-scale industrial online advertising dataset, we show our framework is effective in protecting input privacy while retaining the model utility.", "authors": ["Jiankai Sun", "Yuanshun Yao", "Weihao Gao", "Junyuan Xie", "Chong Wang"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2021-07-21", "url": "https://arxiv.org/abs/2107.09898", "pdf_url": "https://arxiv.org/pdf/2107.09898v1", "arxiv_id": "2107.09898", "doi": null, "citation_count": 32, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3796}
{"id": "142416f74d14ed8362f000dbdf53f4af30c3b9912e356a7cd0e24994eb670651", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease", "abstract": "Deep learning models can enable accurate and efficient disease diagnosis, but have thus far been hampered by the data scarcity present in the medical world. Automated diagnosis studies have been constrained by underpowered single-center datasets, and although some results have shown promise, their generalizability to other institutions remains questionable as the data heterogeneity between institutions is not taken into account. By allowing models to be trained in a distributed manner that preserves patients' privacy, federated learning promises to alleviate these issues, by enabling diligent multi-center studies. We present the first federated learning study on the modality of cardiovascular magnetic resonance (CMR) and use four centers derived from subsets of the M\\&M and ACDC datasets, focusing on the diagnosis of hypertrophic cardiomyopathy (HCM). We adapt a 3D-CNN network pretrained on action recognition and explore two different ways of incorporating shape prior information to the model, and four different data augmentation set-ups, systematically analyzing their impact on the different collaborative learning choices. We show that despite the small size of data (180 subjects derived from four centers), the privacy preserving federated learning achieves promising results that are competitive with traditional centralized learning. We further find that federatively trained models exhibit increased robustness and are more sensitive to domain shift effects.", "authors": ["Akis Linardos", "Kaisar Kushibar", "Sean Walsh", "Polyxeni Gkontra", "Karim Lekadir"], "categories": ["eess.IV", "cs.AI", "cs.LG"], "fields_of_study": ["Engineering", "Computer Science", "Medicine"], "published_date": "2021-07-07", "url": "https://arxiv.org/abs/2107.03901", "pdf_url": "https://arxiv.org/pdf/2107.03901v1", "arxiv_id": "2107.03901", "doi": "10.1038/s41598-022-07186-4", "citation_count": 118, "influential_citation_count": 5, "has_code": true, "code_url": "https://github.com/Linardos/federated-HCM-diagnosis", "venue": "Scientific Reports", "quality_score": 0.5189}
{"id": "fc9887543f6a34fa43ac2e171785cae13f861e77d04f52a99a56d5be49a23275", "sources": ["arxiv", "semantic_scholar"], "title": "DER Forecast using Privacy Preserving Federated Learning", "abstract": "With increasing penetration of Distributed Energy Resources (DERs) in grid edge including renewable generation, flexible loads, and storage, accurate prediction of distributed generation and consumption at the consumer level becomes important. However, DER prediction based on the transmission of customer level data, either repeatedly or in large amounts, is not feasible due to privacy concerns. In this paper, a distributed machine learning approach, Federated Learning, is proposed to carry out DER forecasting using a network of IoT nodes, each of which transmits a model of the consumption and generation patterns without revealing consumer data. We consider a simulation study which includes 1000 DERs, and show that our method leads to an accurate prediction of preserve consumer privacy, while still leading to an accurate forecast. We also evaluate grid-specific performance metrics such as load swings and load curtailment and show that our FL algorithm leads to satisfactory performance. Simulations are also performed on the Pecan street dataset to demonstrate the validity of the proposed approach on real data.", "authors": ["Venkatesh Venkataramanan", "Sridevi Kaza", "Anuradha M. Annaswamy"], "categories": ["eess.SY", "cs.DC", "cs.LG"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2021-07-07", "url": "https://arxiv.org/abs/2107.03248", "pdf_url": "https://arxiv.org/pdf/2107.03248v1", "arxiv_id": "2107.03248", "doi": "10.1109/JIOT.2022.3157299", "citation_count": 56, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "IEEE Internet of Things Journal", "quality_score": 0.439}
{"id": "cefae146e505a53815b2c1ba4df4c362d4945f4de96a8bc78e039adef5e5ca5d", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy Threats Analysis to Secure Federated Learning", "abstract": "Federated learning is emerging as a machine learning technique that trains a model across multiple decentralized parties. It is renowned for preserving privacy as the data never leaves the computational devices, and recent approaches further enhance its privacy by hiding messages transferred in encryption. However, we found that despite the efforts, federated learning remains privacy-threatening, due to its interactive nature across different parties. In this paper, we analyze the privacy threats in industrial-level federated learning frameworks with secure computation, and reveal such threats widely exist in typical machine learning models such as linear regression, logistic regression and decision tree. For the linear and logistic regression, we show through theoretical analysis that it is possible for the attacker to invert the entire private input of the victim, given very few information. For the decision tree model, we launch an attack to infer the range of victim's private inputs. All attacks are evaluated on popular federated learning frameworks and real-world datasets.", "authors": ["Yuchen Li", "Yifan Bao", "Liyao Xiang", "Junhan Liu", "Cen Chen", "Li Wang", "Xinbing Wang"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2021-06-24", "url": "https://arxiv.org/abs/2106.13076", "pdf_url": "https://arxiv.org/pdf/2106.13076v1", "arxiv_id": "2106.13076", "doi": null, "citation_count": 10, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2603}
{"id": "50aa63b4d91f24ddebb5a4a879ad13179bb7be70458a7a19513787ca28ab0f65", "sources": ["arxiv", "semantic_scholar"], "title": "FedXGBoost: Privacy-Preserving XGBoost for Federated Learning", "abstract": "Federated learning is the distributed machine learning framework that enables collaborative training across multiple parties while ensuring data privacy. Practical adaptation of XGBoost, the state-of-the-art tree boosting framework, to federated learning remains limited due to high cost incurred by conventional privacy-preserving methods. To address the problem, we propose two variants of federated XGBoost with privacy guarantee: FedXGBoost-SMM and FedXGBoost-LDP. Our first protocol FedXGBoost-SMM deploys enhanced secure matrix multiplication method to preserve privacy with lossless accuracy and lower overhead than encryption-based techniques. Developed independently, the second protocol FedXGBoost-LDP is heuristically designed with noise perturbation for local differential privacy, and empirically evaluated on real-world and synthetic datasets.", "authors": ["Nhan Khanh Le", "Yang Liu", "Quang Minh Nguyen", "Qingchen Liu", "Fangzhou Liu", "Quanwei Cai", "Sandra Hirche"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2021-06-20", "url": "https://arxiv.org/abs/2106.10662", "pdf_url": "https://arxiv.org/pdf/2106.10662v3", "arxiv_id": "2106.10662", "doi": null, "citation_count": 28, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3656}
{"id": "11230234e0afb1420481957ef0734aa885709c3351025809204e87dcb0ae1684", "sources": ["arxiv", "semantic_scholar"], "title": "Zero-Shot Federated Learning with New Classes for Audio Classification", "abstract": "Federated learning is an effective way of extracting insights from different user devices while preserving the privacy of users. However, new classes with completely unseen data distributions can stream across any device in a federated learning setting, whose data cannot be accessed by the global server or other users. To this end, we propose a unified zero-shot framework to handle these aforementioned challenges during federated learning. We simulate two scenarios here -- 1) when the new class labels are not reported by the user, the traditional FL setting is used; 2) when new class labels are reported by the user, we synthesize Anonymized Data Impressions by calculating class similarity matrices corresponding to each device's new classes followed by unsupervised clustering to distinguish between new classes across different users. Moreover, our proposed framework can also handle statistical heterogeneities in both labels and models across the participating users. We empirically evaluate our framework on-device across different communication rounds (FL iterations) with new classes in both local and global updates, along with heterogeneous labels and models, on two widely used audio classification applications -- keyword spotting and urban sound classification, and observe an average deterministic accuracy increase of ~4.041% and ~4.258% respectively.", "authors": ["Gautham Krishna Gudur", "Satheesh K. Perepu"], "categories": ["cs.LG", "cs.SD", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2021-06-18", "url": "https://arxiv.org/abs/2106.10019", "pdf_url": "https://arxiv.org/pdf/2106.10019v1", "arxiv_id": "2106.10019", "doi": "10.21437/interspeech.2021-2264", "citation_count": 14, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Interspeech", "quality_score": 0.294}
{"id": "631a063bee1e1c6dab96acd70779a56a0496fc09a3ab395eb98fa22da42e53ea", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy Assessment of Federated Learning using Private Personalized Layers", "abstract": "Federated Learning (FL) is a collaborative scheme to train a learning model across multiple participants without sharing data. While FL is a clear step forward towards enforcing users' privacy, different inference attacks have been developed. In this paper, we quantify the utility and privacy trade-off of a FL scheme using private personalized layers. While this scheme has been proposed as local adaptation to improve the accuracy of the model through local personalization, it has also the advantage to minimize the information about the model exchanged with the server. However, the privacy of such a scheme has never been quantified. Our evaluations using motion sensor dataset show that personalized layers speed up the convergence of the model and slightly improve the accuracy for all users compared to a standard FL scheme while better preventing both attribute and membership inferences compared to a FL scheme using local differential privacy.", "authors": ["Théo Jourdan", "Antoine Boutet", "Carole Frindel"], "categories": ["cs.CR", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2021-06-15", "url": "https://arxiv.org/abs/2106.08060", "pdf_url": "https://arxiv.org/pdf/2106.08060v2", "arxiv_id": "2106.08060", "doi": "10.1109/MLSP52302.2021.9596237", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Workshop on Machine Learning for Signal Processing", "quality_score": 0.2386}
{"id": "62f5b1e18cd4e08c5737b1be1a53b1ae048b10dcf6c26e2a73694f9d374bff93", "sources": ["arxiv", "semantic_scholar"], "title": "FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare", "abstract": "The success of machine learning applications often needs a large quantity of data. Recently, federated learning (FL) is attracting increasing attention due to the demand for data privacy and security, especially in the medical field. However, the performance of existing FL approaches often deteriorates when there exist domain shifts among clients, and few previous works focus on personalization in healthcare. In this article, we propose FedHealth 2, an extension of FedHealth \\cite{chen2020fedhealth} to tackle domain shifts and get personalized models for local clients. FedHealth 2 obtains the client similarities via a pretrained model, and then it averages all weighted models with preserving local batch normalization. Wearable activity recognition and COVID-19 auxiliary diagnosis experiments have evaluated that FedHealth 2 can achieve better accuracy (10%+ improvement for activity recognition) and personalized healthcare without compromising privacy and security.", "authors": ["Yiqiang Chen", "Wang Lu", "Jindong Wang", "Xin Qin"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2021-06-02", "url": "https://arxiv.org/abs/2106.01009", "pdf_url": "https://arxiv.org/pdf/2106.01009v2", "arxiv_id": "2106.01009", "doi": null, "citation_count": 18, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3197}
{"id": "911c129a77f8f16826579ac92ceffed1a670077412c280bf760a7b703f7266d6", "sources": ["arxiv", "semantic_scholar"], "title": "PPT: A Privacy-Preserving Global Model Training Protocol for Federated Learning in P2P Networks", "abstract": "The concept of Federated Learning (FL) has emerged as a convergence of machine learning, information, and communication technology. It is vital to the development of machine learning, which is expected to be fully decentralized, privacy-preserving, secure, and robust. However, general federated learning settings with a central server can't meet requirements in decentralized environment. In this paper, we propose a decentralized, secure and privacy-preserving global model training protocol, named PPT, for federated learning in Peer-to-peer (P2P) Networks. PPT uses a one-hop communication form to aggregate local model update parameters and adopts the symmetric cryptosystem to ensure security. It is worth mentioning that PPT modifies the Eschenauer-Gligor (E-G) scheme to distribute keys for encryption. In terms of privacy preservation, PPT generates random noise to disturb local model update parameters. The noise is eliminated ultimately, which ensures the global model performance compared with other noise-based privacy-preserving methods in FL, e.g., differential privacy. PPT also adopts Game Theory to resist collusion attacks. Through extensive analysis, we demonstrate that PPT various security threats and preserve user privacy. Ingenious experiments demonstrate the utility and efficiency as well.", "authors": ["Qian Chen", "Zilong Wang", "Wenjing Zhang", "Xiaodong Lin"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2021-05-30", "url": "https://arxiv.org/abs/2105.14408", "pdf_url": "https://arxiv.org/pdf/2105.14408v2", "arxiv_id": "2105.14408", "doi": "10.1016/j.cose.2022.102966", "citation_count": 18, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Computers & security", "quality_score": 0.3197}
{"id": "61fa2820fb416e1a2ec3a0264023a2a34e88c968241f063abfff5fed9707cd3e", "sources": ["arxiv", "semantic_scholar"], "title": "HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning", "abstract": "Federated learning (FL) enables multiple clients to jointly train a global model under the coordination of a central server. Although FL is a privacy-aware paradigm, where raw data sharing is not required, recent studies have shown that FL might leak the private data of a client through the model parameters shared with the server or the other clients. In this paper, we present the HyFed framework, which enhances the privacy of FL while preserving the utility of the global model. HyFed provides developers with a generic API to develop federated, privacy-preserving algorithms. HyFed supports both simulation and federated operation modes and its source code is publicly available at https://github.com/tum-aimed/hyfed.", "authors": ["Reza Nasirigerdeh", "Reihaneh Torkzadehmahani", "Julian Matschinske", "Jan Baumbach", "Daniel Rueckert", "Georgios Kaissis"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2021-05-21", "url": "https://arxiv.org/abs/2105.10545", "pdf_url": "https://arxiv.org/pdf/2105.10545v2", "arxiv_id": "2105.10545", "doi": null, "citation_count": 13, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/tum-aimed/hyfed", "venue": "arXiv.org", "quality_score": 0.2865}
{"id": "f0fee8dce80089dc55d1a26ecfa565b5eb629a686e90f25e67816b0ec5cd8851", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Federated Learning on Partitioned Attributes", "abstract": "Real-world data is usually segmented by attributes and distributed across different parties. Federated learning empowers collaborative training without exposing local data or models. As we demonstrate through designed attacks, even with a small proportion of corrupted data, an adversary can accurately infer the input attributes. We introduce an adversarial learning based procedure which tunes a local model to release privacy-preserving intermediate representations. To alleviate the accuracy decline, we propose a defense method based on the forward-backward splitting algorithm, which respectively deals with the accuracy loss and privacy loss in the forward and backward gradient descent steps, achieving the two objectives simultaneously. Extensive experiments on a variety of datasets have shown that our defense significantly mitigates privacy leakage with negligible impact on the federated learning task.", "authors": ["Shuang Zhang", "Liyao Xiang", "Xi Yu", "Pengzhi Chu", "Yingqi Chen", "Chen Cen", "Li Wang"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2021-04-29", "url": "https://arxiv.org/abs/2104.14383", "pdf_url": "https://arxiv.org/pdf/2104.14383v1", "arxiv_id": "2104.14383", "doi": null, "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "d074e4ef030c267d51ca33d80f8f21e32bc27eb59b3effb05f068ffd108fa73f", "sources": ["arxiv", "semantic_scholar"], "title": "From Distributed Machine Learning to Federated Learning: A Survey", "abstract": "In recent years, data and computing resources are typically distributed in the devices of end users, various regions or organizations. Because of laws or regulations, the distributed data and computing resources cannot be directly shared among different regions or organizations for machine learning tasks. Federated learning emerges as an efficient approach to exploit distributed data and computing resources, so as to collaboratively train machine learning models, while obeying the laws and regulations and ensuring data security and data privacy. In this paper, we provide a comprehensive survey of existing works for federated learning. We propose a functional architecture of federated learning systems and a taxonomy of related techniques. Furthermore, we present the distributed training, data communication, and security of FL systems. Finally, we analyze their limitations and propose future research directions.", "authors": ["Ji Liu", "Jizhou Huang", "Yang Zhou", "Xuhong Li", "Shilei Ji", "Haoyi Xiong", "Dejing Dou"], "categories": ["cs.DC", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2021-04-29", "url": "https://arxiv.org/abs/2104.14362", "pdf_url": "https://arxiv.org/pdf/2104.14362v4", "arxiv_id": "2104.14362", "doi": "10.1007/s10115-022-01664-x", "citation_count": 348, "influential_citation_count": 10, "has_code": false, "code_url": null, "venue": "Knowledge and Information Systems", "quality_score": 0.6357}
{"id": "00d9ff0ce623e1ce0ff801017e0fa316c97a52619913ef84171097deaec14738", "sources": ["arxiv", "semantic_scholar"], "title": "PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments", "abstract": "We propose and implement a Privacy-preserving Federated Learning ($PPFL$) framework for mobile systems to limit privacy leakages in federated learning. Leveraging the widespread presence of Trusted Execution Environments (TEEs) in high-end and mobile devices, we utilize TEEs on clients for local training, and on servers for secure aggregation, so that model/gradient updates are hidden from adversaries. Challenged by the limited memory size of current TEEs, we leverage greedy layer-wise training to train each model's layer inside the trusted area until its convergence. The performance evaluation of our implementation shows that $PPFL$ can significantly improve privacy while incurring small system overheads at the client-side. In particular, $PPFL$ can successfully defend the trained model against data reconstruction, property inference, and membership inference attacks. Furthermore, it can achieve comparable model utility with fewer communication rounds (0.54$\\times$) and a similar amount of network traffic (1.002$\\times$) compared to the standard federated learning of a complete model. This is achieved while only introducing up to ~15% CPU time, ~18% memory usage, and ~21% energy consumption overhead in $PPFL$'s client-side.", "authors": ["Fan Mo", "Hamed Haddadi", "Kleomenis Katevas", "Eduard Marin", "Diego Perino", "Nicolas Kourtellis"], "categories": ["cs.CR", "cs.DC", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2021-04-29", "url": "https://arxiv.org/abs/2104.14380", "pdf_url": "https://arxiv.org/pdf/2104.14380v2", "arxiv_id": "2104.14380", "doi": "10.1145/3458864.3466628", "citation_count": 312, "influential_citation_count": 14, "has_code": false, "code_url": null, "venue": "ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services", "quality_score": 0.6239}
{"id": "89496bb63f05677023bb6b178ef50b5c033f3f992f0710a1ba3f23e72bb89a57", "sources": ["arxiv", "semantic_scholar"], "title": "A Graph Federated Architecture with Privacy Preserving Learning", "abstract": "Federated learning involves a central processor that works with multiple agents to find a global model. The process consists of repeatedly exchanging estimates, which results in the diffusion of information pertaining to the local private data. Such a scheme can be inconvenient when dealing with sensitive data, and therefore, there is a need for the privatization of the algorithms. Furthermore, the current architecture of a server connected to multiple clients is highly sensitive to communication failures and computational overloads at the server. Thus in this work, we develop a private multi-server federated learning scheme, which we call graph federated learning. We use cryptographic and differential privacy concepts to privatize the federated learning algorithm that we extend to the graph structure. We study the effect of privatization on the performance of the learning algorithm for general private schemes that can be modeled as additive noise. We show under convexity and Lipschitz conditions, that the privatized process matches the performance of the non-private algorithm, even when we increase the noise variance.", "authors": ["Elsa Rizk", "Ali H. Sayed"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2021-04-26", "url": "https://arxiv.org/abs/2104.13215", "pdf_url": "https://arxiv.org/pdf/2104.13215v1", "arxiv_id": "2104.13215", "doi": "10.1109/SPAWC51858.2021.9593148", "citation_count": 26, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "International Workshop on Signal Processing Advances in Wireless Communications", "quality_score": 0.3578}
{"id": "66bc949ea2cd31e34f0d7de291a24a4486b56e2c8e05dd4fe795a2cb8f63b4c1", "sources": ["arxiv", "semantic_scholar"], "title": "Blockchain based Privacy-Preserved Federated Learning for Medical Images: A Case Study of COVID-19 CT Scans", "abstract": "Medical health care centers are envisioned as a promising paradigm to handle the massive volume of data of COVID-19 patients using artificial intelligence (AI). Traditionally, AI techniques often require centralized data collection and training the model in a single organization, which is most common weakness due to the privacy and security of raw data communication. To solve this challenging task, we propose a blockchain-based federated learning framework that provides collaborative data training solutions by coordinating multiple hospitals to train and share encrypted federated models without leakage of data privacy. The blockchain ledger technology provides the decentralization of federated learning model without any central server. The proposed homomorphic encryption scheme encrypts and decrypts the gradients of model to preserve the privacy. More precisely, the proposed framework: i) train the local model by a novel capsule network to segmentation and classify COVID-19 images, ii) then use the homomorphic encryption scheme to secure the local model that encrypts and decrypts the gradients, and finally the model is shared over a decentralized platform through proposed blockchain-based federated learning algorithm. The integration of blockchain and federated learning leads to a new paradigm for medical image data sharing in the decentralized network. The conducted experimental resultsdemonstrate the performance of the proposed scheme.", "authors": ["Rajesh Kumar", "WenYong Wang", "Cheng Yuan", "Jay Kumar", " Zakria", "He Qing", "Ting Yang", "Abdullah Aman Khan"], "categories": ["cs.CR", "cs.LG", "eess.IV"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2021-04-22", "url": "https://arxiv.org/abs/2104.10903", "pdf_url": "https://arxiv.org/pdf/2104.10903v2", "arxiv_id": "2104.10903", "doi": null, "citation_count": 11, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2698}
{"id": "a97b0fc9083b5f2dd300163abf11f6fffb3bb9e714a1b5a12607ba6683b13fd4", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning for Malware Detection in IoT Devices", "abstract": "This work investigates the possibilities enabled by federated learning concerning IoT malware detection and studies security issues inherent to this new learning paradigm. In this context, a framework that uses federated learning to detect malware affecting IoT devices is presented. N-BaIoT, a dataset modeling network traffic of several real IoT devices while affected by malware, has been used to evaluate the proposed framework. Both supervised and unsupervised federated models (multi-layer perceptron and autoencoder) able to detect malware affecting seen and unseen IoT devices of N-BaIoT have been trained and evaluated. Furthermore, their performance has been compared to two traditional approaches. The first one lets each participant locally train a model using only its own data, while the second consists of making the participants share their data with a central entity in charge of training a global model. This comparison has shown that the use of more diverse and large data, as done in the federated and centralized methods, has a considerable positive impact on the model performance. Besides, the federated models, while preserving the participant's privacy, show similar results as the centralized ones. As an additional contribution and to measure the robustness of the federated approach, an adversarial setup with several malicious participants poisoning the federated model has been considered. The baseline model aggregation averaging step used in most federated learning algorithms appears highly vulnerable to different attacks, even with a single adversary. The performance of other model aggregation functions acting as countermeasures is thus evaluated under the same attack scenarios. These functions provide a significant improvement against malicious participants, but more efforts are still needed to make federated approaches robust.", "authors": ["Valerian Rey", "Pedro Miguel Sánchez Sánchez", "Alberto Huertas Celdrán", "Gérôme Bovet", "Martin Jaggi"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2021-04-15", "url": "https://arxiv.org/abs/2104.09994", "pdf_url": "https://arxiv.org/pdf/2104.09994v3", "arxiv_id": "2104.09994", "doi": "10.1016/j.comnet.2021.108693", "citation_count": 365, "influential_citation_count": 24, "has_code": false, "code_url": null, "venue": "Rey, V., Sánchez, P. M. S., Celdrán, A. H., & Bovet, G. (2022). Federated learning for malware detection in iot devices. Computer Networks, 108693", "quality_score": 0.699}
{"id": "68179738b068b207f0adab26e0a657adc11fa10106c8771eb4e734d61d240b7f", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Causal Federated Learning For Enhanced Robustness and Privacy", "abstract": "Federated Learning is an emerging privacy-preserving distributed machine learning approach to building a shared model by performing distributed training locally on participating devices (clients) and aggregating the local models into a global one. As this approach prevents data collection and aggregation, it helps in reducing associated privacy risks to a great extent. However, the data samples across all participating clients are usually not independent and identically distributed (non-iid), and Out of Distribution(OOD) generalization for the learned models can be poor. Besides this challenge, federated learning also remains vulnerable to various attacks on security wherein a few malicious participating entities work towards inserting backdoors, degrading the generated aggregated model as well as inferring the data owned by participating entities. In this paper, we propose an approach for learning invariant (causal) features common to all participating clients in a federated learning setup and analyze empirically how it enhances the Out of Distribution (OOD) accuracy as well as the privacy of the final learned model.", "authors": ["Sreya Francis", "Irene Tenison", "Irina Rish"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2021-04-14", "url": "https://arxiv.org/abs/2104.06557", "pdf_url": "https://arxiv.org/pdf/2104.06557v1", "arxiv_id": "2104.06557", "doi": null, "citation_count": 16, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3495}
{"id": "55ac9b9b66e071fa4a8319ca7bb31a8e8d94bdfb68eb9855a64bc6500079c1c8", "sources": ["arxiv", "semantic_scholar"], "title": "FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks", "abstract": "Graph Neural Network (GNN) research is rapidly growing thanks to the capacity of GNNs in learning distributed representations from graph-structured data. However, centralizing a massive amount of real-world graph data for GNN training is prohibitive due to privacy concerns, regulation restrictions, and commercial competitions. Federated learning (FL), a trending distributed learning paradigm, provides possibilities to solve this challenge while preserving data privacy. Despite recent advances in vision and language domains, there is no suitable platform for the FL of GNNs. To this end, we introduce FedGraphNN, an open FL benchmark system that can facilitate research on federated GNNs. FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support. Particularly for the datasets, we collect, preprocess, and partition 36 datasets from 7 domains, including both publicly available ones and specifically obtained ones such as hERG and Tencent. Our empirical analysis showcases the utility of our benchmark system, while exposing significant challenges in graph FL: federated GNNs perform worse in most datasets with a non-IID split than centralized GNNs; the GNN model that attains the best result in the centralized setting may not maintain its advantage in the FL setting. These results imply that more research efforts are needed to unravel the mystery behind federated GNNs. Moreover, our system performance analysis demonstrates that the FedGraphNN system is computationally efficient and secure to large-scale graphs datasets. We maintain the source code at https://github.com/FedML-AI/FedGraphNN.", "authors": ["Chaoyang He", "Keshav Balasubramanian", "Emir Ceyani", "Carl Yang", "Han Xie", "Lichao Sun", "Lifang He", "Liangwei Yang", "Philip S. Yu", "Yu Rong", "Peilin Zhao", "Junzhou Huang", "Murali Annavaram", "Salman Avestimehr"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2021-04-14", "url": "https://arxiv.org/abs/2104.07145", "pdf_url": "https://arxiv.org/pdf/2104.07145v2", "arxiv_id": "2104.07145", "doi": null, "citation_count": 2, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/FedML-AI/FedGraphNN", "venue": null, "quality_score": 0.1193}
{"id": "85aa71b4c35dd1eee372fb6c30b2ddb1eed24ae99d0e5d6546bb1ca0c5121eb6", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryption", "abstract": "With the advance of machine learning and the internet of things (IoT), security and privacy have become key concerns in mobile services and networks. Transferring data to a central unit violates privacy as well as protection of sensitive data while increasing bandwidth demands.Federated learning mitigates this need to transfer local data by sharing model updates only. However, data leakage still remains an issue. In this paper, we propose xMK-CKKS, a multi-key homomorphic encryption protocol to design a novel privacy-preserving federated learning scheme. In this scheme, model updates are encrypted via an aggregated public key before sharing with a server for aggregation. For decryption, collaboration between all participating devices is required. This scheme prevents privacy leakage from publicly shared information in federated learning, and is robust to collusion between $k<N-1$ participating devices and the server. Our experimental evaluation demonstrates that the scheme preserves model accuracy against traditional federated learning as well as secure federated learning with homomorphic encryption (MK-CKKS, Paillier) and reduces computational cost compared to Paillier based federated learning. The average energy consumption is 2.4 Watts, so that it is suited to IoT scenarios.", "authors": ["Jing Ma", "Si-Ahmed Naas", "Stephan Sigg", "Xixiang Lyu"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2021-04-14", "url": "https://arxiv.org/abs/2104.06824", "pdf_url": "https://arxiv.org/pdf/2104.06824v1", "arxiv_id": "2104.06824", "doi": "10.1002/int.22818", "citation_count": 372, "influential_citation_count": 18, "has_code": false, "code_url": null, "venue": "International Journal of Intelligent Systems", "quality_score": 0.6429}
{"id": "6463e7921862e276322a315b7b7a1937e4451c4a78f832c6d6aa68e5aae2b2ad", "sources": ["arxiv", "semantic_scholar"], "title": "PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN", "abstract": "We introduce PyVertical, a framework supporting vertical federated learning using split neural networks. The proposed framework allows a data scientist to train neural networks on data features vertically partitioned across multiple owners while keeping raw data on an owner's device. To link entities shared across different datasets' partitions, we use Private Set Intersection on IDs associated with data points. To demonstrate the validity of the proposed framework, we present the training of a simple dual-headed split neural network for a MNIST classification task, with data samples vertically distributed across two data owners and a data scientist.", "authors": ["Daniele Romanini", "Adam James Hall", "Pavlos Papadopoulos", "Tom Titcombe", "Abbas Ismail", "Tudor Cebere", "Robert Sandmann", "Robin Roehm", "Michael A. Hoeh"], "categories": ["cs.LG", "cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2021-04-01", "url": "https://arxiv.org/abs/2104.00489", "pdf_url": "https://arxiv.org/pdf/2104.00489v3", "arxiv_id": "2104.00489", "doi": null, "citation_count": 114, "influential_citation_count": 5, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5152}
{"id": "ff4baef8f3125f0ebf01dd2bdbdb6a40e019be938a81293057387afc988d9556", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy and Trust Redefined in Federated Machine Learning", "abstract": "A common privacy issue in traditional machine learning is that data needs to be disclosed for the training procedures. In situations with highly sensitive data such as healthcare records, accessing this information is challenging and often prohibited. Luckily, privacy-preserving technologies have been developed to overcome this hurdle by distributing the computation of the training and ensuring the data privacy to their owners. The distribution of the computation to multiple participating entities introduces new privacy complications and risks. In this paper, we present a privacy-preserving decentralised workflow that facilitates trusted federated learning among participants. Our proof-of-concept defines a trust framework instantiated using decentralised identity technologies being developed under Hyperledger projects Aries/Indy/Ursa. Only entities in possession of Verifiable Credentials issued from the appropriate authorities are able to establish secure, authenticated communication channels authorised to participate in a federated learning workflow related to mental health data.", "authors": ["Pavlos Papadopoulos", "Will Abramson", "Adam J. Hall", "Nikolaos Pitropakis", "William J. Buchanan"], "categories": ["cs.CR", "cs.CY", "cs.DC", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2021-03-29", "url": "https://arxiv.org/abs/2103.15753", "pdf_url": "https://arxiv.org/pdf/2103.15753v2", "arxiv_id": "2103.15753", "doi": "10.3390/make3020017", "citation_count": 50, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "Machine Learning and Knowledge Extraction", "quality_score": 0.4269}
{"id": "3eeb26664b84a9c2f4a217d086297d1750b3b7dc7cebe83309d7d7b018d12012", "sources": ["arxiv", "semantic_scholar"], "title": "SoK: Privacy-Preserving Collaborative Tree-based Model Learning", "abstract": "Tree-based models are among the most efficient machine learning techniques for data mining nowadays due to their accuracy, interpretability, and simplicity. The recent orthogonal needs for more data and privacy protection call for collaborative privacy-preserving solutions. In this work, we survey the literature on distributed and privacy-preserving training of tree-based models and we systematize its knowledge based on four axes: the learning algorithm, the collaborative model, the protection mechanism, and the threat model. We use this to identify the strengths and limitations of these works and provide for the first time a framework analyzing the information leakage occurring in distributed tree-based model learning.", "authors": ["Sylvain Chatel", "Apostolos Pyrgelis", "Juan Ramon Troncoso-Pastoriza", "Jean-Pierre Hubaux"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2021-03-16", "url": "https://arxiv.org/abs/2103.08987", "pdf_url": "https://arxiv.org/pdf/2103.08987v2", "arxiv_id": "2103.08987", "doi": "10.2478/popets-2021-0043", "citation_count": 19, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Proceedings on Privacy Enhancing Technologies", "quality_score": 0.3253}
{"id": "918b0e7fd93a2467b4412497d0ddbe0a767d9a03fcf8dfea0333b2cec55f644c", "sources": ["arxiv", "semantic_scholar"], "title": "PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization", "abstract": "Federated learning (FL) has become a prevalent distributed machine learning paradigm with improved privacy. After learning, the resulting federated model should be further personalized to each different client. While several methods have been proposed to achieve personalization, they are typically limited to a single local device, which may incur bias or overfitting since data in a single device is extremely limited. In this paper, we attempt to realize personalization beyond a single client. The motivation is that during FL, there may exist many clients with similar data distribution, and thus the personalization performance could be significantly boosted if these similar clients can cooperate with each other. Inspired by this, this paper introduces a new concept called federated adaptation, targeting at adapting the trained model in a federated manner to achieve better personalization results. However, the key challenge for federated adaptation is that we could not outsource any raw data from the client during adaptation, due to privacy concerns. In this paper, we propose PFA, a framework to accomplish Privacy-preserving Federated Adaptation. PFA leverages the sparsity property of neural networks to generate privacy-preserving representations and uses them to efficiently identify clients with similar data distributions. Based on the grouping results, PFA conducts an FL process in a group-wise way on the federated model to accomplish the adaptation. For evaluation, we manually construct several practical FL datasets based on public datasets in order to simulate both the class-imbalance and background-difference conditions. Extensive experiments on these datasets and popular model architectures demonstrate the effectiveness of PFA, outperforming other state-of-the-art methods by a large margin while ensuring user privacy. We will release our code at: https://github.com/lebyni/PFA.", "authors": ["Bingyan Liu", "Yao Guo", "Xiangqun Chen"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2021-03-02", "url": "https://arxiv.org/abs/2103.01548", "pdf_url": "https://arxiv.org/pdf/2103.01548v2", "arxiv_id": "2103.01548", "doi": "10.1145/3442381.3449847", "citation_count": 119, "influential_citation_count": 3, "has_code": true, "code_url": "https://github.com/lebyni/PFA", "venue": "The Web Conference", "quality_score": 0.5198}
{"id": "7a0f82d2fd13ae4a42eabb5d60cf27079a6ed485df987e421773b928c46dbf05", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Wireless Federated Learning Exploiting Inherent Hardware Impairments", "abstract": "We consider a wireless federated learning system where multiple data holder edge devices collaborate to train a global model via sharing their parameter updates with an honest-but-curious parameter server. We demonstrate that the inherent hardware-induced distortion perturbing the model updates of the edge devices can be exploited as a privacy-preserving mechanism. In particular, we model the distortion as power-dependent additive Gaussian noise and present a power allocation strategy that provides privacy guarantees within the framework of differential privacy. We conduct numerical experiments to evaluate the performance of the proposed power allocation scheme under different levels of hardware impairments.", "authors": ["Sina Rezaei Aghdam", "Ehsan Amid", "Marija Furdek", "Alexandre Graell i Amat"], "categories": ["cs.IT", "eess.SP"], "fields_of_study": ["Computer Science", "Engineering", "Mathematics"], "published_date": "2021-02-21", "url": "https://arxiv.org/abs/2102.10639", "pdf_url": "https://arxiv.org/pdf/2102.10639v2", "arxiv_id": "2102.10639", "doi": "10.1109/camad52502.2021.9617764", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks", "quality_score": 0.1505}
{"id": "fb62ed5c740b9ba419daec9464f51c05df64197559a857356b70a294b3b06c0a", "sources": ["arxiv", "semantic_scholar"], "title": "When Crowdsensing Meets Federated Learning: Privacy-Preserving Mobile Crowdsensing System", "abstract": "Mobile crowdsensing (MCS) is an emerging sensing data collection pattern with scalability, low deployment cost, and distributed characteristics. Traditional MCS systems suffer from privacy concerns and fair reward distribution. Moreover, existing privacy-preserving MCS solutions usually focus on the privacy protection of data collection rather than that of data processing. To tackle faced problems of MCS, in this paper, we integrate federated learning (FL) into MCS and propose a privacy-preserving MCS system, called \\textsc{CrowdFL}. Specifically, in order to protect privacy, participants locally process sensing data via federated learning and only upload encrypted training models. Particularly, a privacy-preserving federated averaging algorithm is proposed to average encrypted training models. To reduce computation and communication overhead of restraining dropped participants, discard and retransmission strategies are designed. Besides, a privacy-preserving posted pricing incentive mechanism is designed, which tries to break the dilemma of privacy protection and data evaluation. Theoretical analysis and experimental evaluation on a practical MCS application demonstrate the proposed \\textsc{CrowdFL} can effectively protect participants privacy and is feasible and efficient.", "authors": ["Bowen Zhao", "Ximeng Liu", "Wei-neng Chen"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2021-02-20", "url": "https://arxiv.org/abs/2102.10109", "pdf_url": "https://arxiv.org/pdf/2102.10109v1", "arxiv_id": "2102.10109", "doi": null, "citation_count": 24, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3495}
{"id": "39ace3e12620e66ab5bae9ff12207412a7057a3c6e07d2e3ae182120f3dafa8a", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Self-Taught Federated Learning for Heterogeneous Data", "abstract": "Many application scenarios call for training a machine learning model among multiple participants. Federated learning (FL) was proposed to enable joint training of a deep learning model using the local data in each party without revealing the data to others. Among various types of FL methods, vertical FL is a category to handle data sources with the same ID space and different feature spaces. However, existing vertical FL methods suffer from limitations such as restrictive neural network structure, slow training speed, and often lack the ability to take advantage of data with unmatched IDs. In this work, we propose an FL method called self-taught federated learning to address the aforementioned issues, which uses unsupervised feature extraction techniques for distributed supervised deep learning tasks. In this method, only latent variables are transmitted to other parties for model training, while privacy is preserved by storing the data and parameters of activations, weights, and biases locally. Extensive experiments are performed to evaluate and demonstrate the validity and efficiency of the proposed method.", "authors": ["Kai-Fung Chu", "Lintao Zhang"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2021-02-11", "url": "https://arxiv.org/abs/2102.05883", "pdf_url": "https://arxiv.org/pdf/2102.05883v1", "arxiv_id": "2102.05883", "doi": null, "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2698}
{"id": "09d80cea97b262e7bb85da277ae72f37880b132b18b12ee0d26fb668bd06cd33", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Reconstruction: Partially Local Federated Learning", "abstract": "Personalization methods in federated learning aim to balance the benefits of federated and local training for data availability, communication cost, and robustness to client heterogeneity. Approaches that require clients to communicate all model parameters can be undesirable due to privacy and communication constraints. Other approaches require always-available or stateful clients, impractical in large-scale cross-device settings. We introduce Federated Reconstruction, the first model-agnostic framework for partially local federated learning suitable for training and inference at scale. We motivate the framework via a connection to model-agnostic meta learning, empirically demonstrate its performance over existing approaches for collaborative filtering and next word prediction, and release an open-source library for evaluating approaches in this setting. We also describe the successful deployment of this approach at scale for federated collaborative filtering in a mobile keyboard application.", "authors": ["Karan Singhal", "Hakim Sidahmed", "Zachary Garrett", "Shanshan Wu", "Keith Rush", "Sushant Prakash"], "categories": ["cs.LG", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2021-02-05", "url": "https://arxiv.org/abs/2102.03448", "pdf_url": "https://arxiv.org/pdf/2102.03448v6", "arxiv_id": "2102.03448", "doi": null, "citation_count": 170, "influential_citation_count": 12, "has_code": true, "code_url": "https://github.com/google-research/federated/tree/master/reconstruction", "venue": "Neural Information Processing Systems", "quality_score": 0.5582}
{"id": "32e46e42789be230f6f5b8b039e290dc2194bbaeb525b4f9e477fe8cd820ab6f", "sources": ["arxiv", "semantic_scholar"], "title": "Differential Privacy Meets Federated Learning under Communication Constraints", "abstract": "The performance of federated learning systems is bottlenecked by communication costs and training variance. The communication overhead problem is usually addressed by three communication-reduction techniques, namely, model compression, partial device participation, and periodic aggregation, at the cost of increased training variance. Different from traditional distributed learning systems, federated learning suffers from data heterogeneity (since the devices sample their data from possibly different distributions), which induces additional variance among devices during training. Various variance-reduced training algorithms have been introduced to combat the effects of data heterogeneity, while they usually cost additional communication resources to deliver necessary control information. Additionally, data privacy remains a critical issue in FL, and thus there have been attempts at bringing Differential Privacy to this framework as a mediator between utility and privacy requirements. This paper investigates the trade-offs between communication costs and training variance under a resource-constrained federated system theoretically and experimentally, and how communication reduction techniques interplay in a differentially private setting. The results provide important insights into designing practical privacy-aware federated learning systems.", "authors": ["Nima Mohammadi", "Jianan Bai", "Qiang Fan", "Yifei Song", "Yang Yi", "Lingjia Liu"], "categories": ["cs.LG", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2021-01-28", "url": "https://arxiv.org/abs/2101.12240", "pdf_url": "https://arxiv.org/pdf/2101.12240v1", "arxiv_id": "2101.12240", "doi": "10.1109/JIOT.2021.3101991", "citation_count": 42, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "IEEE Internet of Things Journal", "quality_score": 0.4084}
{"id": "4aa13e70d116f9ce8dfdafda9e0c439c51e97046c86a2b2af3feaf57a9ac6f83", "sources": ["arxiv", "semantic_scholar"], "title": "Dopamine: Differentially Private Federated Learning on Medical Data", "abstract": "While rich medical datasets are hosted in hospitals distributed across the world, concerns on patients' privacy is a barrier against using such data to train deep neural networks (DNNs) for medical diagnostics. We propose Dopamine, a system to train DNNs on distributed datasets, which employs federated learning (FL) with differentially-private stochastic gradient descent (DPSGD), and, in combination with secure aggregation, can establish a better trade-off between differential privacy (DP) guarantee and DNN's accuracy than other approaches. Results on a diabetic retinopathy~(DR) task show that Dopamine provides a DP guarantee close to the centralized training counterpart, while achieving a better classification accuracy than FL with parallel DP where DPSGD is applied without coordination. Code is available at https://github.com/ipc-lab/private-ml-for-health.", "authors": ["Mohammad Malekzadeh", "Burak Hasircioglu", "Nitish Mital", "Kunal Katarya", "Mehmet Emre Ozfatura", "Deniz Gündüz"], "categories": ["cs.LG", "cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2021-01-27", "url": "https://arxiv.org/abs/2101.11693", "pdf_url": "https://arxiv.org/pdf/2101.11693v2", "arxiv_id": "2101.11693", "doi": null, "citation_count": 62, "influential_citation_count": 7, "has_code": true, "code_url": "https://github.com/ipc-lab/private-ml-for-health", "venue": "arXiv.org", "quality_score": 0.4515}
{"id": "ef231e303342e6fbd0b78e282e3aeaa9aadfeadcdbfed733310ed599d9641cd5", "sources": ["arxiv", "semantic_scholar"], "title": "Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning", "abstract": "This paper introduces the first provably accurate algorithms for differentially private, top-down decision tree learning in the distributed setting (Balcan et al., 2012). We propose DP-TopDown, a general privacy preserving decision tree learning algorithm, and present two distributed implementations. Our first method NoisyCounts naturally extends the single machine algorithm by using the Laplace mechanism. Our second method LocalRNM significantly reduces communication and added noise by performing local optimization at each data holder. We provide the first utility guarantees for differentially private top-down decision tree learning in both the single machine and distributed settings. These guarantees show that the error of the privately-learned decision tree quickly goes to zero provided that the dataset is sufficiently large. Our extensive experiments on real datasets illustrate the trade-offs of privacy, accuracy and generalization when learning private decision trees in the distributed setting.", "authors": ["Kaiwen Wang", "Travis Dick", "Maria-Florina Balcan"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-12-19", "url": "https://arxiv.org/abs/2012.10602", "pdf_url": "https://arxiv.org/pdf/2012.10602v3", "arxiv_id": "2012.10602", "doi": null, "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2113}
{"id": "6fc86d3304a6c099db6504076eb36c7a06fd7ba5f85fcad894b39e099ab3aeff", "sources": ["arxiv", "semantic_scholar"], "title": "CosSGD: Communication-Efficient Federated Learning with a Simple Cosine-Based Quantization", "abstract": "Federated learning is a promising framework to mitigate data privacy and computation concerns. However, the communication cost between the server and clients has become the major bottleneck for successful deployment. Despite notable progress in gradient compression, the existing quantization methods require further improvement when low-bits compression is applied, especially the overall systems often degenerate a lot when quantization are applied in double directions to compress model weights and gradients. In this work, we propose a simple cosine-based nonlinear quantization and achieve impressive results in compressing round-trip communication costs. We are not only able to compress model weights and gradients at higher ratios than previous methods, but also achieve competing model performance at the same time. Further, our approach is highly suitable for federated learning problems since it has low computational complexity and requires only a little additional data to recover the compressed information. Extensive experiments have been conducted on image classification and brain tumor semantic segmentation using the CIFAR-10, and BraTS datasets where we show state-of-the-art effectiveness and impressive communication efficiency.", "authors": ["Yang He", "Hui-Po Wang", "Maximilian Zenk", "Mario Fritz"], "categories": ["cs.LG", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2020-12-15", "url": "https://arxiv.org/abs/2012.08241", "pdf_url": "https://arxiv.org/pdf/2012.08241v2", "arxiv_id": "2012.08241", "doi": null, "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2698}
{"id": "3842c078adf1a81c46e43a5c053fe2d1c08eba23ad5e5f110b5b0b7e7b510a69", "sources": ["arxiv", "semantic_scholar"], "title": "Robustness Threats of Differential Privacy", "abstract": "Differential privacy (DP) is a gold-standard concept of measuring and guaranteeing privacy in data analysis. It is well-known that the cost of adding DP to deep learning model is its accuracy. However, it remains unclear how it affects robustness of the model. Standard neural networks are not robust to different input perturbations: either adversarial attacks or common corruptions. In this paper, we empirically observe an interesting trade-off between privacy and robustness of neural networks. We experimentally demonstrate that networks, trained with DP, in some settings might be even more vulnerable in comparison to non-private versions. To explore this, we extensively study different robustness measurements, including FGSM and PGD adversaries, distance to linear decision boundaries, curvature profile, and performance on a corrupted dataset. Finally, we study how the main ingredients of differentially private neural networks training, such as gradient clipping and noise addition, affect (decrease and increase) the robustness of the model.", "authors": ["Nurislam Tursynbek", "Aleksandr Petiushko", "Ivan Oseledets"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2020-12-14", "url": "https://arxiv.org/abs/2012.07828", "pdf_url": "https://arxiv.org/pdf/2012.07828v3", "arxiv_id": "2012.07828", "doi": null, "citation_count": 20, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3306}
{"id": "a013879d456e3f32fcc693a0273a485722fb63d21ad28039d847303b6dc2ddbb", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-preserving Decentralized Aggregation for Federated Learning", "abstract": "Federated learning is a promising framework for learning over decentralized data spanning multiple regions. This approach avoids expensive central training data aggregation cost and can improve privacy because distributed sites do not have to reveal privacy-sensitive data. In this paper, we develop a privacy-preserving decentralized aggregation protocol for federated learning. We formulate the distributed aggregation protocol with the Alternating Direction Method of Multiplier (ADMM) and examine its privacy weakness. Unlike prior work that use Differential Privacy or homomorphic encryption for privacy, we develop a protocol that controls communication among participants in each round of aggregation to minimize privacy leakage. We establish its privacy guarantee against an honest-but-curious adversary. We also propose an efficient algorithm to construct such a communication pattern, inspired by combinatorial block design theory. Our secure aggregation protocol based on this novel group communication pattern design leads to an efficient algorithm for federated training with privacy guarantees. We evaluate our federated training algorithm on image classification and next-word prediction applications over benchmark datasets with 9 and 15 distributed sites. Evaluation results show that our algorithm performs comparably to the standard centralized federated learning method while preserving privacy; the degradation in test accuracy is only up to 0.73%.", "authors": ["Beomyeol Jeon", "S. M. Ferdous", "Muntasir Raihan Rahman", "Anwar Walid"], "categories": ["cs.LG", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2020-12-13", "url": "https://arxiv.org/abs/2012.07183", "pdf_url": "https://arxiv.org/pdf/2012.07183v2", "arxiv_id": "2012.07183", "doi": "10.1109/INFOCOMWKSHPS51825.2021.9484437", "citation_count": 79, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "Conference on Computer Communications Workshops", "quality_score": 0.4758}
{"id": "1a4eddb05f171777d2e709510437a83abc588693c1d5398af3cccd8ba487370d", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Mimic Learning for Privacy Preserving Intrusion Detection", "abstract": "Internet of things (IoT) devices are prone to attacks due to the limitation of their privacy and security components. These attacks vary from exploiting backdoors to disrupting the communication network of the devices. Intrusion Detection Systems (IDS) play an essential role in ensuring information privacy and security of IoT devices against these attacks. Recently, deep learning-based IDS techniques are becoming more prominent due to their high classification accuracy. However, conventional deep learning techniques jeopardize user privacy due to the transfer of user data to a centralized server. Federated learning (FL) is a popular privacy-preserving decentralized learning method. FL enables training models locally at the edge devices and transferring local models to a centralized server instead of transferring sensitive data. Nevertheless, FL can suffer from reverse engineering ML attacks that can learn information about the user's data from model. To overcome the problem of reverse engineering, mimic learning is another way to preserve the privacy of ML-based IDS. In mimic learning, a student model is trained with the public dataset, which is labeled with the teacher model that is trained by sensitive user data. In this work, we propose a novel approach that combines the advantages of FL and mimic learning, namely federated mimic learning to create a distributed IDS while minimizing the risk of jeopardizing users' privacy, and benchmark its performance compared to other ML-based IDS techniques using NSL-KDD dataset. Our results show that we can achieve 98.11% detection accuracy with federated mimic learning.", "authors": ["Noor Ali Al-Athba Al-Marri", "Bekir Sait Ciftler", "Mohamed Abdallah"], "categories": ["cs.NI"], "fields_of_study": ["Computer Science"], "published_date": "2020-12-13", "url": "https://arxiv.org/abs/2012.06974", "pdf_url": "https://arxiv.org/pdf/2012.06974v1", "arxiv_id": "2012.06974", "doi": "10.1109/BlackSeaCom48709.2020.9234959", "citation_count": 59, "influential_citation_count": 7, "has_code": false, "code_url": null, "venue": "International Black Sea Conference on Communications and Networking", "quality_score": 0.4515}
{"id": "b94061823285a28ea3ce09528c044e30e86fac46d4f4571305a41af963918afc", "sources": ["arxiv", "semantic_scholar"], "title": "FAT: Federated Adversarial Training", "abstract": "Federated learning (FL) is one of the most important paradigms addressing privacy and data governance issues in machine learning (ML). Adversarial training has emerged, so far, as the most promising approach against evasion threats on ML models. In this paper, we take the first known steps towards federated adversarial training (FAT) combining both methods to reduce the threat of evasion during inference while preserving the data privacy during training. We investigate the effectiveness of the FAT protocol for idealised federated settings using MNIST, Fashion-MNIST, and CIFAR10, and provide first insights on stabilising the training on the LEAF benchmark dataset which specifically emulates a federated learning environment. We identify challenges with this natural extension of adversarial training with regards to achieved adversarial robustness and further examine the idealised settings in the presence of clients undermining model convergence. We find that Trimmed Mean and Bulyan defences can be compromised and we were able to subvert Krum with a novel distillation based attack which presents an apparently \"robust\" model to the defender while in fact the model fails to provide robustness against simple attack modifications.", "authors": ["Giulio Zizzo", "Ambrish Rawat", "Mathieu Sinn", "Beat Buesser"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2020-12-03", "url": "https://arxiv.org/abs/2012.01791", "pdf_url": "https://arxiv.org/pdf/2012.01791v1", "arxiv_id": "2012.01791", "doi": null, "citation_count": 60, "influential_citation_count": 24, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.699}
{"id": "462c5cb0dd4ed303d7bf8299557664b2bb83757897fa79cc3cd37c94c553e157", "sources": ["arxiv", "semantic_scholar"], "title": "Advancements of federated learning towards privacy preservation: from federated learning to split learning", "abstract": "In the distributed collaborative machine learning (DCML) paradigm, federated learning (FL) recently attracted much attention due to its applications in health, finance, and the latest innovations such as industry 4.0 and smart vehicles. FL provides privacy-by-design. It trains a machine learning model collaboratively over several distributed clients (ranging from two to millions) such as mobile phones, without sharing their raw data with any other participant. In practical scenarios, all clients do not have sufficient computing resources (e.g., Internet of Things), the machine learning model has millions of parameters, and its privacy between the server and the clients while training/testing is a prime concern (e.g., rival parties). In this regard, FL is not sufficient, so split learning (SL) is introduced. SL is reliable in these scenarios as it splits a model into multiple portions, distributes them among clients and server, and trains/tests their respective model portions to accomplish the full model training/testing. In SL, the participants do not share both data and their model portions to any other parties, and usually, a smaller network portion is assigned to the clients where data resides. Recently, a hybrid of FL and SL, called splitfed learning, is introduced to elevate the benefits of both FL (faster training/testing time) and SL (model split and training). Following the developments from FL to SL, and considering the importance of SL, this chapter is designed to provide extensive coverage in SL and its variants. The coverage includes fundamentals, existing findings, integration with privacy measures such as differential privacy, open problems, and code implementation.", "authors": ["Chandra Thapa", "M. A. P. Chamikara", "Seyit A. Camtepe"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2020-11-25", "url": "https://arxiv.org/abs/2011.14818", "pdf_url": "https://arxiv.org/pdf/2011.14818v1", "arxiv_id": "2011.14818", "doi": "10.1007/978-3-030-70604-3_4", "citation_count": 108, "influential_citation_count": 5, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.5094}
{"id": "5aefda98d430ed429ed2c6ebc2d040a41b14b6a2dfb2552a4984f800cffec3b8", "sources": ["arxiv", "semantic_scholar"], "title": "Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learning", "abstract": "Homomorphic encryption is a very useful gradient protection technique used in privacy preserving federated learning. However, existing encrypted federated learning systems need a trusted third party to generate and distribute key pairs to connected participants, making them unsuited for federated learning and vulnerable to security risks. Moreover, encrypting all model parameters is computationally intensive, especially for large machine learning models such as deep neural networks. In order to mitigate these issues, we develop a practical, computationally efficient encryption based protocol for federated deep learning, where the key pairs are collaboratively generated without the help of a third party. By quantization of the model parameters on the clients and an approximated aggregation on the server, the proposed method avoids encryption and decryption of the entire model. In addition, a threshold based secret sharing technique is designed so that no one can hold the global private key for decryption, while aggregated ciphertexts can be successfully decrypted by a threshold number of clients even if some clients are offline. Our experimental results confirm that the proposed method significantly reduces the communication costs and computational complexity compared to existing encrypted federated learning without compromising the performance and security.", "authors": ["Hangyu Zhu", "Rui Wang", "Yaochu Jin", "Kaitai Liang", "Jianting Ning"], "categories": ["cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2020-11-25", "url": "https://arxiv.org/abs/2011.12623", "pdf_url": "https://arxiv.org/pdf/2011.12623v1", "arxiv_id": "2011.12623", "doi": "10.1016/J.NEUCOM.2021.08.062", "citation_count": 62, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "Neurocomputing", "quality_score": 0.4498}
{"id": "ac0e5fed6c4a59d65bffff5c725d091f9857bdef7812f58887900373b1e15f4c", "sources": ["arxiv", "semantic_scholar"], "title": "A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning", "abstract": "Federated learning (FL) is an emerging practical framework for effective and scalable machine learning among multiple participants, such as end users, organizations and companies. However, most existing FL or distributed learning frameworks have not well addressed two important issues together: collaborative fairness and adversarial robustness (e.g. free-riders and malicious participants). In conventional FL, all participants receive the global model (equal rewards), which might be unfair to the high-contributing participants. Furthermore, due to the lack of a safeguard mechanism, free-riders or malicious adversaries could game the system to access the global model for free or to sabotage it. In this paper, we propose a novel Robust and Fair Federated Learning (RFFL) framework to achieve collaborative fairness and adversarial robustness simultaneously via a reputation mechanism. RFFL maintains a reputation for each participant by examining their contributions via their uploaded gradients (using vector similarity) and thus identifies non-contributing or malicious participants to be removed. Our approach differentiates itself by not requiring any auxiliary/validation dataset. Extensive experiments on benchmark datasets show that RFFL can achieve high fairness and is very robust to different types of adversaries while achieving competitive predictive accuracy.", "authors": ["Xinyi Xu", "Lingjuan Lyu"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-11-20", "url": "https://arxiv.org/abs/2011.10464", "pdf_url": "https://arxiv.org/pdf/2011.10464v2", "arxiv_id": "2011.10464", "doi": null, "citation_count": 96, "influential_citation_count": 13, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.5731}
{"id": "a81eb735591c3f1c6c309f00f12d80d5fb6f1541c7bc61298445bbbd41a540d1", "sources": ["arxiv", "semantic_scholar"], "title": "Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning", "abstract": "We present a multi-language, cross-platform, open-source library for asymmetric private set intersection (PSI) and PSI-Cardinality (PSI-C). Our protocol combines traditional DDH-based PSI and PSI-C protocols with compression based on Bloom filters that helps reduce communication in the asymmetric setting. Currently, our library supports C++, C, Go, WebAssembly, JavaScript, Python, and Rust, and runs on both traditional hardware (x86) and browser targets. We further apply our library to two use cases: (i) a privacy-preserving contact tracing protocol that is compatible with existing approaches, but improves their privacy guarantees, and (ii) privacy-preserving machine learning on vertically partitioned data.", "authors": ["Nick Angelou", "Ayoub Benaissa", "Bogdan Cebere", "William Clark", "Adam James Hall", "Michael A. Hoeh", "Daniel Liu", "Pavlos Papadopoulos", "Robin Roehm", "Robert Sandmann", "Phillipp Schoppmann", "Tom Titcombe"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2020-11-18", "url": "https://arxiv.org/abs/2011.09350", "pdf_url": "https://arxiv.org/pdf/2011.09350v1", "arxiv_id": "2011.09350", "doi": null, "citation_count": 30, "influential_citation_count": 3, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3728}
{"id": "35fc52f02974ee20b3531e55c99fc3e8c4aca54020e189e3f461751fd15b16d3", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations", "abstract": "Dimensionality Reduction is a commonly used element in a machine learning pipeline that helps to extract important features from high-dimensional data. In this work, we explore an alternative federated learning system that enables integration of dimensionality reduced representations of distributed data prior to a supervised learning task, thus avoiding model sharing among the parties. We compare the performance of this approach on image classification tasks to three alternative frameworks: centralized machine learning, individual machine learning, and Federated Averaging, and analyze potential use cases for a federated learning system without model sharing. Our results show that our approach can achieve similar accuracy as Federated Averaging and performs better than Federated Averaging in a small-user setting.", "authors": ["Anna Bogdanova", "Akie Nakai", "Yukihiko Okada", "Akira Imakura", "Tetsuya Sakurai"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2020-11-13", "url": "https://arxiv.org/abs/2011.06803", "pdf_url": "https://arxiv.org/pdf/2011.06803v1", "arxiv_id": "2011.06803", "doi": null, "citation_count": 18, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3197}
{"id": "252a3b56c1720b5d32c5cf72cd176152af98bf0e67ce2346c97352f52bf2f0d0", "sources": ["arxiv", "semantic_scholar"], "title": "Fed-Focal Loss for imbalanced data classification in Federated Learning", "abstract": "The Federated Learning setting has a central server coordinating the training of a model on a network of devices. One of the challenges is variable training performance when the dataset has a class imbalance. In this paper, we address this by introducing a new loss function called Fed-Focal Loss. We propose to address the class imbalance by reshaping cross-entropy loss such that it down-weights the loss assigned to well-classified examples along the lines of focal loss. Additionally, by leveraging a tunable sampling framework, we take into account selective client model contributions on the central server to further focus the detector during training and hence improve its robustness. Using a detailed experimental analysis with the VIRTUAL (Variational Federated Multi-Task Learning) approach, we demonstrate consistently superior performance in both the balanced and unbalanced scenarios for MNIST, FEMNIST, VSN and HAR benchmarks. We obtain a more than 9% (absolute percentage) improvement in the unbalanced MNIST benchmark. We further show that our technique can be adopted across multiple Federated Learning algorithms to get improvements.", "authors": ["Dipankar Sarkar", "Ankur Narang", "Sumit Rai"], "categories": ["cs.LG", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2020-11-12", "url": "https://arxiv.org/abs/2011.06283", "pdf_url": "https://arxiv.org/pdf/2011.06283v1", "arxiv_id": "2011.06283", "doi": null, "citation_count": 63, "influential_citation_count": 6, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4515}
{"id": "4588e9807f8d41f55855764c4941b47d9b0ae28a75eb980f755e49772df9357b", "sources": ["arxiv", "semantic_scholar"], "title": "Heterogeneous Data-Aware Federated Learning", "abstract": "Federated learning (FL) is an appealing concept to perform distributed training of Neural Networks (NN) while keeping data private. With the industrialization of the FL framework, we identify several problems hampering its successful deployment, such as presence of non i.i.d data, disjoint classes, signal multi-modality across datasets. In this work, we address these problems by proposing a novel method that not only (1) aggregates generic model parameters (e.g. a common set of task generic NN layers) on server (e.g. in traditional FL), but also (2) keeps a set of parameters (e.g, a set of task specific NN layer) specific to each client. We validate our method on the traditionally used public benchmarks (e.g., Femnist) as well as on our proprietary collected dataset (i.e., traffic classification). Results show the benefit of our method, with significant advantage on extreme cases.", "authors": ["Lixuan Yang", "Cedric Beliard", "Dario Rossi"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2020-11-12", "url": "https://arxiv.org/abs/2011.06393", "pdf_url": "https://arxiv.org/pdf/2011.06393v1", "arxiv_id": "2011.06393", "doi": null, "citation_count": 18, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3197}
{"id": "bd2fd1495ebbb0e85db4d6d2d4d26ad35f2d9191c7c9772a4b4a61cde8a0bfdb", "sources": ["arxiv", "semantic_scholar"], "title": "Mitigating Leakage in Federated Learning with Trusted Hardware", "abstract": "In federated learning, multiple parties collaborate in order to train a global model over their respective datasets. Even though cryptographic primitives (e.g., homomorphic encryption) can help achieve data privacy in this setting, some partial information may still be leaked across parties if this is done non-judiciously. In this work, we study the federated learning framework of SecureBoost [Cheng et al., FL@IJCAI'19] as a specific such example, demonstrate a leakage-abuse attack based on its leakage profile, and experimentally evaluate the effectiveness of our attack. We then propose two secure versions relying on trusted execution environments. We implement and benchmark our protocols to demonstrate that they are 1.2-5.4X faster in computation and need 5-49X less communication than SecureBoost.", "authors": ["Javad Ghareh Chamani", "Dimitrios Papadopoulos"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2020-11-10", "url": "https://arxiv.org/abs/2011.04948", "pdf_url": "https://arxiv.org/pdf/2011.04948v3", "arxiv_id": "2011.04948", "doi": null, "citation_count": 13, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2865}
{"id": "0100443e02642834badca9d5bda782592bd49929ed704af6e8df5ef1482c6933", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy Preservation in Federated Learning: An insightful survey from the GDPR Perspective", "abstract": "Along with the blooming of AI and Machine Learning-based applications and services, data privacy and security have become a critical challenge. Conventionally, data is collected and aggregated in a data centre on which machine learning models are trained. This centralised approach has induced severe privacy risks to personal data leakage, misuse, and abuse. Furthermore, in the era of the Internet of Things and big data in which data is essentially distributed, transferring a vast amount of data to a data centre for processing seems to be a cumbersome solution. This is not only because of the difficulties in transferring and sharing data across data sources but also the challenges on complying with rigorous data protection regulations and complicated administrative procedures such as the EU General Data Protection Regulation (GDPR). In this respect, Federated learning (FL) emerges as a prospective solution that facilitates distributed collaborative learning without disclosing original training data whilst naturally complying with the GDPR. Recent research has demonstrated that retaining data and computation on-device in FL is not sufficient enough for privacy-guarantee. This is because ML model parameters exchanged between parties in an FL system still conceal sensitive information, which can be exploited in some privacy attacks. Therefore, FL systems shall be empowered by efficient privacy-preserving techniques to comply with the GDPR. This article is dedicated to surveying on the state-of-the-art privacy-preserving techniques which can be employed in FL in a systematic fashion, as well as how these techniques mitigate data security and privacy risks. Furthermore, we provide insights into the challenges along with prospective approaches following the GDPR regulatory guidelines that an FL system shall implement to comply with the GDPR.", "authors": ["Nguyen Truong", "Kai Sun", "Siyao Wang", "Florian Guitton", "Yike Guo"], "categories": ["cs.CR", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2020-11-10", "url": "https://arxiv.org/abs/2011.05411", "pdf_url": "https://arxiv.org/pdf/2011.05411v5", "arxiv_id": "2011.05411", "doi": null, "citation_count": 12, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2785}
{"id": "cad0182fbd63dc5b3f7777f81d20b811316c5b2e4697f7c3b60063fc7b28bcd5", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving XGBoost Inference", "abstract": "Although machine learning (ML) is widely used for predictive tasks, there are important scenarios in which ML cannot be used or at least cannot achieve its full potential. A major barrier to adoption is the sensitive nature of predictive queries. Individual users may lack sufficiently rich datasets to train accurate models locally but also be unwilling to send sensitive queries to commercial services that vend such models. One central goal of privacy-preserving machine learning (PPML) is to enable users to submit encrypted queries to a remote ML service, receive encrypted results, and decrypt them locally. We aim at developing practical solutions for real-world privacy-preserving ML inference problems. In this paper, we propose a privacy-preserving XGBoost prediction algorithm, which we have implemented and evaluated empirically on AWS SageMaker. Experimental results indicate that our algorithm is efficient enough to be used in real ML production environments.", "authors": ["Xianrui Meng", "Joan Feigenbaum"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2020-11-09", "url": "https://arxiv.org/abs/2011.04789", "pdf_url": "https://arxiv.org/pdf/2011.04789v4", "arxiv_id": "2011.04789", "doi": null, "citation_count": 15, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.301}
{"id": "d57e8d6428a3c55e169e7efdcffc194cf4326989d47ed2425c2344e6be79e88f", "sources": ["arxiv", "semantic_scholar"], "title": "FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networks", "abstract": "Federated Learning (FL) and Split Learning (SL) are privacy-preserving Machine-Learning (ML) techniques that enable training ML models over data distributed among clients without requiring direct access to their raw data. Existing FL and SL approaches work on horizontally or vertically partitioned data and cannot handle sequentially partitioned data where segments of multiple-segment sequential data are distributed across clients. In this paper, we propose a novel federated split learning framework, FedSL, to train models on distributed sequential data. The most common ML models to train on sequential data are Recurrent Neural Networks (RNNs). Since the proposed framework is privacy-preserving, segments of multiple-segment sequential data cannot be shared between clients or between clients and server. To circumvent this limitation, we propose a novel SL approach tailored for RNNs. A RNN is split into sub-networks, and each sub-network is trained on one client containing single segments of multiple-segment training sequences. During local training, the sub-networks on different clients communicate with each other to capture latent dependencies between consecutive segments of multiple-segment sequential data on different clients, but without sharing raw data or complete model parameters. After training local sub-networks with local sequential data segments, all clients send their sub-networks to a federated server where sub-networks are aggregated to generate a global model. The experimental results on simulated and real-world datasets demonstrate that the proposed method successfully trains models on distributed sequential data, while preserving privacy, and outperforms previous FL and centralized learning approaches in terms of achieving higher accuracy in fewer communication rounds.", "authors": ["Ali Abedi", "Shehroz S. Khan"], "categories": ["cs.LG", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2020-11-06", "url": "https://arxiv.org/abs/2011.03180", "pdf_url": "https://arxiv.org/pdf/2011.03180v3", "arxiv_id": "2011.03180", "doi": "10.1007/s11042-023-15184-5", "citation_count": 89, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Multimedia tools and applications", "quality_score": 0.4886}
{"id": "57637c07746bcbbcb5dcbb8e7fa090c76c85a64a1d04a3a939274c2ffe875cb3", "sources": ["arxiv", "semantic_scholar"], "title": "From Distributed Machine Learning To Federated Learning: In The View Of Data Privacy And Security", "abstract": "Federated learning is an improved version of distributed machine learning that further offloads operations which would usually be performed by a central server. The server becomes more like an assistant coordinating clients to work together rather than micro-managing the workforce as in traditional DML. One of the greatest advantages of federated learning is the additional privacy and security guarantees it affords. Federated learning architecture relies on smart devices, such as smartphones and IoT sensors, that collect and process their own data, so sensitive information never has to leave the client device. Rather, clients train a sub-model locally and send an encrypted update to the central server for aggregation into the global model. These strong privacy guarantees make federated learning an attractive choice in a world where data breaches and information theft are common and serious threats. This survey outlines the landscape and latest developments in data privacy and security for federated learning. We identify the different mechanisms used to provide privacy and security, such as differential privacy, secure multi-party computation and secure aggregation. We also survey the current attack models, identifying the areas of vulnerability and the strategies adversaries use to penetrate federated systems. The survey concludes with a discussion on the open challenges and potential directions of future work in this increasingly popular learning paradigm.", "authors": ["Sheng Shen", "Tianqing Zhu", "Di Wu", "Wei Wang", "Wanlei Zhou"], "categories": ["cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2020-10-19", "url": "https://arxiv.org/abs/2010.09258", "pdf_url": "https://arxiv.org/pdf/2010.09258v1", "arxiv_id": "2010.09258", "doi": "10.1002/cpe.6002", "citation_count": 111, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Concurrency and Computation", "quality_score": 0.5123}
{"id": "4aa58e1d87d24bfcc7b9c07671ec0802edc22ef959e10f10ca7a1fd96d00b121", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Object Detection & Localization Using Distributed Machine Learning: A Case Study of Infant Eyeblink Conditioning", "abstract": "Distributed machine learning is becoming a popular model-training method due to privacy, computational scalability, and bandwidth capacities. In this work, we explore scalable distributed-training versions of two algorithms commonly used in object detection. A novel distributed training algorithm using Mean Weight Matrix Aggregation (MWMA) is proposed for Linear Support Vector Machine (L-SVM) object detection based in Histogram of Orientated Gradients (HOG). In addition, a novel Weighted Bin Aggregation (WBA) algorithm is proposed for distributed training of Ensemble of Regression Trees (ERT) landmark localization. Both algorithms do not restrict the location of model aggregation and allow custom architectures for model distribution. For this work, a Pool-Based Local Training and Aggregation (PBLTA) architecture for both algorithms is explored. The application of both algorithms in the medical field is examined using a paradigm from the fields of psychology and neuroscience - eyeblink conditioning with infants - where models need to be trained on facial images while protecting participant privacy. Using distributed learning, models can be trained without sending image data to other nodes. The custom software has been made available for public use on GitHub: https://github.com/SLWZwaard/DMT. Results show that the aggregation of models for the HOG algorithm using MWMA not only preserves the accuracy of the model but also allows for distributed learning with an accuracy increase of 0.9% compared with traditional learning. Furthermore, WBA allows for ERT model aggregation with an accuracy increase of 8% when compared to single-node models.", "authors": ["Stefan Zwaard", "Henk-Jan Boele", "Hani Alers", "Christos Strydis", "Casey Lew-Williams", "Zaid Al-Ars"], "categories": ["cs.LG", "cs.CR", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2020-10-14", "url": "https://arxiv.org/abs/2010.07259", "pdf_url": "https://arxiv.org/pdf/2010.07259v1", "arxiv_id": "2010.07259", "doi": null, "citation_count": 3, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/SLWZwaard/DMT", "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "d28f27e5a990901a3d211bc7e922bad532ab46c329b87e8fb5e65c8a6cb85868", "sources": ["arxiv", "semantic_scholar"], "title": "FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning", "abstract": "Recent attacks on federated learning demonstrate that keeping the training data on clients' devices does not provide sufficient privacy, as the model parameters shared by clients can leak information about their training data. A 'secure aggregation' protocol enables the server to aggregate clients' models in a privacy-preserving manner. However, existing secure aggregation protocols incur high computation/communication costs, especially when the number of model parameters is larger than the number of clients participating in an iteration -- a typical scenario in federated learning. In this paper, we propose a secure aggregation protocol, FastSecAgg, that is efficient in terms of computation and communication, and robust to client dropouts. The main building block of FastSecAgg is a novel multi-secret sharing scheme, FastShare, based on the Fast Fourier Transform (FFT), which may be of independent interest. FastShare is information-theoretically secure, and achieves a trade-off between the number of secrets, privacy threshold, and dropout tolerance. Riding on the capabilities of FastShare, we prove that FastSecAgg is (i) secure against the server colluding with 'any' subset of some constant fraction (e.g. $\\sim10\\%$) of the clients in the honest-but-curious setting; and (ii) tolerates dropouts of a 'random' subset of some constant fraction (e.g. $\\sim10\\%$) of the clients. FastSecAgg achieves significantly smaller computation cost than existing schemes while achieving the same (orderwise) communication cost. In addition, it guarantees security against adaptive adversaries, which can perform client corruptions dynamically during the execution of the protocol.", "authors": ["Swanand Kadhe", "Nived Rajaraman", "O. Ozan Koyluoglu", "Kannan Ramchandran"], "categories": ["cs.CR", "cs.IT", "cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-09-23", "url": "https://arxiv.org/abs/2009.11248", "pdf_url": "https://arxiv.org/pdf/2009.11248v1", "arxiv_id": "2009.11248", "doi": null, "citation_count": 207, "influential_citation_count": 13, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5795}
{"id": "f553d5048443fd48ed55b5072bced5d9ba29b7c4b17e1de497216d327ecd88cc", "sources": ["arxiv", "semantic_scholar"], "title": "Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare", "abstract": "Federated learning (FL) has enabled training models collaboratively from multiple data owning parties without sharing their data. Given the privacy regulations of patient's healthcare data, learning-based systems in healthcare can greatly benefit from privacy-preserving FL approaches. However, typical model aggregation methods in FL are sensitive to local model updates, which may lead to failure in learning a robust and accurate global model. In this work, we implement and evaluate different robust aggregation methods in FL applied to healthcare data. Furthermore, we show that such methods can detect and discard faulty or malicious local clients during training. We run two sets of experiments using two real-world healthcare datasets for training medical diagnosis classification tasks. Each dataset is used to simulate the performance of three different robust FL aggregation strategies when facing different poisoning attacks. The results show that privacy preserving methods can be successfully applied alongside Byzantine-robust aggregation techniques. We observed in particular how using differential privacy (DP) did not significantly impact the final learning convergence of the different aggregation strategies.", "authors": ["Matei Grama", "Maria Musat", "Luis Muñoz-González", "Jonathan Passerat-Palmbach", "Daniel Rueckert", "Amir Alansary"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2020-09-17", "url": "https://arxiv.org/abs/2009.08294", "pdf_url": "https://arxiv.org/pdf/2009.08294v1", "arxiv_id": "2009.08294", "doi": null, "citation_count": 59, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4445}
{"id": "659a5cda4f22d624f5b22b5fd079d16783722f49b51f10b0c167cace946f4410", "sources": ["arxiv", "semantic_scholar"], "title": "POSEIDON: Privacy-Preserving Federated Neural Network Learning", "abstract": "In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an $N$-party, federated learning setting. We propose a novel system, POSEIDON, the first of its kind in the regime of privacy-preserving neural network training. It employs multiparty lattice-based cryptography to preserve the confidentiality of the training data, the model, and the evaluation data, under a passive-adversary model and collusions between up to $N-1$ parties. To efficiently execute the secure backpropagation algorithm for training neural networks, we provide a generic packing approach that enables Single Instruction, Multiple Data (SIMD) operations on encrypted data. We also introduce arbitrary linear transformations within the cryptographic bootstrapping operation, optimizing the costly cryptographic computations over the parties, and we define a constrained optimization problem for choosing the cryptographic parameters. Our experimental results show that POSEIDON achieves accuracy similar to centralized or decentralized non-private approaches and that its computation and communication overhead scales linearly with the number of parties. POSEIDON trains a 3-layer neural network on the MNIST dataset with 784 features and 60K samples distributed among 10 parties in less than 2 hours.", "authors": ["Sinem Sav", "Apostolos Pyrgelis", "Juan R. Troncoso-Pastoriza", "David Froelicher", "Jean-Philippe Bossuat", "Joao Sa Sousa", "Jean-Pierre Hubaux"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2020-09-01", "url": "https://arxiv.org/abs/2009.00349", "pdf_url": "https://arxiv.org/pdf/2009.00349v3", "arxiv_id": "2009.00349", "doi": "10.14722/NDSS.2021.24119", "citation_count": 188, "influential_citation_count": 18, "has_code": false, "code_url": null, "venue": "Network and Distributed System Security Symposium", "quality_score": 0.6394}
{"id": "fb2151186494deb4d92cc9aa5b401603d22e604a822de0eb623b81bc55418cfd", "sources": ["arxiv", "semantic_scholar"], "title": "A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations", "abstract": "Privacy-preserving recommendations are recently gaining momentum, since the decentralized user data is increasingly harder to collect, by recommendation service providers, due to the serious concerns over user privacy and data security. This situation is further exacerbated by the strict government regulations such as Europe's General Data Privacy Regulations(GDPR). Federated Learning(FL) is a newly developed privacy-preserving machine learning paradigm to bridge data repositories without compromising data security and privacy. Thus many federated recommendation(FedRec) algorithms have been proposed to realize personalized privacy-preserving recommendations. However, existing FedRec algorithms, mostly extended from traditional collaborative filtering(CF) method, cannot address cold-start problem well. In addition, their performance overhead w.r.t. model accuracy, trained in a federated setting, is often non-negligible comparing to centralized recommendations. This paper studies this issue and presents FL-MV-DSSM, a generic content-based federated multi-view recommendation framework that not only addresses the cold-start problem, but also significantly boosts the recommendation performance by learning a federated model from multiple data source for capturing richer user-level features. The new federated multi-view setting, proposed by FL-MV-DSSM, opens new usage models and brings in new security challenges to FL in recommendation scenarios. We prove the security guarantees of \\xxx, and empirical evaluations on FL-MV-DSSM and its variations with public datasets demonstrate its effectiveness. Our codes will be released if this paper is accepted.", "authors": ["Mingkai Huang", "Hao Li", "Bing Bai", "Chang Wang", "Kun Bai", "Fei Wang"], "categories": ["cs.IR", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2020-08-25", "url": "https://arxiv.org/abs/2008.10808", "pdf_url": "https://arxiv.org/pdf/2008.10808v1", "arxiv_id": "2008.10808", "doi": null, "citation_count": 38, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3978}
{"id": "b5d96192ac606537f441f9afb75ae54b2a8d45892a30d69bc9122b604d38bed5", "sources": ["arxiv", "semantic_scholar"], "title": "Heterogeneous Federated Learning", "abstract": "Federated learning learns from scattered data by fusing collaborative models from local nodes. However, due to chaotic information distribution, the model fusion may suffer from structural misalignment with regard to unmatched parameters. In this work, we propose a novel federated learning framework to resolve this issue by establishing a firm structure-information alignment across collaborative models. Specifically, we design a feature-oriented regulation method ({$Ψ$-Net}) to ensure explicit feature information allocation in different neural network structures. Applying this regulating method to collaborative models, matchable structures with similar feature information can be initialized at the very early training stage. During the federated learning process under either IID or non-IID scenarios, dedicated collaboration schemes further guarantee ordered information distribution with definite structure matching, so as the comprehensive model alignment. Eventually, this framework effectively enhances the federated learning applicability to extensive heterogeneous settings, while providing excellent convergence speed, accuracy, and computation/communication efficiency.", "authors": ["Fuxun Yu", "Weishan Zhang", "Zhuwei Qin", "Zirui Xu", "Di Wang", "Chenchen Liu", "Zhi Tian", "Xiang Chen"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-08-15", "url": "https://arxiv.org/abs/2008.06767", "pdf_url": "https://arxiv.org/pdf/2008.06767v2", "arxiv_id": "2008.06767", "doi": null, "citation_count": 32, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3796}
{"id": "00e965065d84ac260e5d71c1b1716a784ed1d98d80ab95cba67b7481728d6aa3", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learning", "abstract": "The privacy-preserving federated learning for vertically partitioned data has shown promising results as the solution of the emerging multi-party joint modeling application, in which the data holders (such as government branches, private finance and e-business companies) collaborate throughout the learning process rather than relying on a trusted third party to hold data. However, existing federated learning algorithms for vertically partitioned data are limited to synchronous computation. To improve the efficiency when the unbalanced computation/communication resources are common among the parties in the federated learning system, it is essential to develop asynchronous training algorithms for vertically partitioned data while keeping the data privacy. In this paper, we propose an asynchronous federated SGD (AFSGD-VP) algorithm and its SVRG and SAGA variants on the vertically partitioned data. Moreover, we provide the convergence analyses of AFSGD-VP and its SVRG and SAGA variants under the condition of strong convexity. We also discuss their model privacy, data privacy, computational complexities and communication costs. To the best of our knowledge, AFSGD-VP and its SVRG and SAGA variants are the first asynchronous federated learning algorithms for vertically partitioned data. Extensive experimental results on a variety of vertically partitioned datasets not only verify the theoretical results of AFSGD-VP and its SVRG and SAGA variants, but also show that our algorithms have much higher efficiency than the corresponding synchronous algorithms.", "authors": ["Bin Gu", "An Xu", "Zhouyuan Huo", "Cheng Deng", "Heng Huang"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-08-14", "url": "https://arxiv.org/abs/2008.06233", "pdf_url": "https://arxiv.org/pdf/2008.06233v1", "arxiv_id": "2008.06233", "doi": null, "citation_count": 31, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3763}
{"id": "dc0874e3d4c19b361eaafa12b0fabaf945a728d735ca4a8385e90f47f2ca2c3f", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy Preserving Vertical Federated Learning for Tree-based Models", "abstract": "Federated learning (FL) is an emerging paradigm that enables multiple organizations to jointly train a model without revealing their private data to each other. This paper studies {\\it vertical} federated learning, which tackles the scenarios where (i) collaborating organizations own data of the same set of users but with disjoint features, and (ii) only one organization holds the labels. We propose Pivot, a novel solution for privacy preserving vertical decision tree training and prediction, ensuring that no intermediate information is disclosed other than those the clients have agreed to release (i.e., the final tree model and the prediction output). Pivot does not rely on any trusted third party and provides protection against a semi-honest adversary that may compromise $m-1$ out of $m$ clients. We further identify two privacy leakages when the trained decision tree model is released in plaintext and propose an enhanced protocol to mitigate them. The proposed solution can also be extended to tree ensemble models, e.g., random forest (RF) and gradient boosting decision tree (GBDT) by treating single decision trees as building blocks. Theoretical and experimental analysis suggest that Pivot is efficient for the privacy achieved.", "authors": ["Yuncheng Wu", "Shaofeng Cai", "Xiaokui Xiao", "Gang Chen", "Beng Chin Ooi"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2020-08-14", "url": "https://arxiv.org/abs/2008.06170", "pdf_url": "https://arxiv.org/pdf/2008.06170v1", "arxiv_id": "2008.06170", "doi": "10.14778/3407790.3407811", "citation_count": 263, "influential_citation_count": 21, "has_code": false, "code_url": null, "venue": "Proceedings of the VLDB Endowment", "quality_score": 0.6712}
{"id": "81bcbd7af3b911099a3bd9510a37dd97ff3d2a0997b42514ecf5e334bcddf7af", "sources": ["arxiv", "semantic_scholar"], "title": "Flower: A Friendly Federated Learning Research Framework", "abstract": "Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store the data in the cloud. However, FL is difficult to implement realistically, both in terms of scale and systems heterogeneity. Although there are a number of research frameworks available to simulate FL algorithms, they do not support the study of scalable FL workloads on heterogeneous edge devices. In this paper, we present Flower -- a comprehensive FL framework that distinguishes itself from existing platforms by offering new facilities to execute large-scale FL experiments and consider richly heterogeneous FL device scenarios. Our experiments show Flower can perform FL experiments up to 15M in client size using only a pair of high-end GPUs. Researchers can then seamlessly migrate experiments to real devices to examine other parts of the design space. We believe Flower provides the community with a critical new tool for FL study and development.", "authors": ["Daniel J. Beutel", "Taner Topal", "Akhil Mathur", "Xinchi Qiu", "Javier Fernandez-Marques", "Yan Gao", "Lorenzo Sani", "Kwing Hei Li", "Titouan Parcollet", "Pedro Porto Buarque de Gusmão", "Nicholas D. Lane"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-07-28", "url": "https://arxiv.org/abs/2007.14390", "pdf_url": "https://arxiv.org/pdf/2007.14390v5", "arxiv_id": "2007.14390", "doi": null, "citation_count": 1282, "influential_citation_count": 138, "has_code": true, "code_url": null, "venue": null, "quality_score": 1.0}
{"id": "c69c2d419d9a751cd31660902c7d7fd4313f09def2ffd681894267b19a848f55", "sources": ["arxiv", "semantic_scholar"], "title": "Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge", "abstract": "Scaling up the convolutional neural network (CNN) size (e.g., width, depth, etc.) is known to effectively improve model accuracy. However, the large model size impedes training on resource-constrained edge devices. For instance, federated learning (FL) may place undue burden on the compute capability of edge nodes, even though there is a strong practical need for FL due to its privacy and confidentiality properties. To address the resource-constrained reality of edge devices, we reformulate FL as a group knowledge transfer training algorithm, called FedGKT. FedGKT designs a variant of the alternating minimization approach to train small CNNs on edge nodes and periodically transfer their knowledge by knowledge distillation to a large server-side CNN. FedGKT consolidates several advantages into a single framework: reduced demand for edge computation, lower communication bandwidth for large CNNs, and asynchronous training, all while maintaining model accuracy comparable to FedAvg. We train CNNs designed based on ResNet-56 and ResNet-110 using three distinct datasets (CIFAR-10, CIFAR-100, and CINIC-10) and their non-I.I.D. variants. Our results show that FedGKT can obtain comparable or even slightly higher accuracy than FedAvg. More importantly, FedGKT makes edge training affordable. Compared to the edge training using FedAvg, FedGKT demands 9 to 17 times less computational power (FLOPs) on edge devices and requires 54 to 105 times fewer parameters in the edge CNN. Our source code is released at FedML (https://fedml.ai).", "authors": ["Chaoyang He", "Murali Annavaram", "Salman Avestimehr"], "categories": ["cs.LG", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2020-07-28", "url": "https://arxiv.org/abs/2007.14513", "pdf_url": "https://arxiv.org/pdf/2007.14513v4", "arxiv_id": "2007.14513", "doi": null, "citation_count": 24, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3495}
{"id": "131df23a12abf1b3b8cadaa44b4beb9dfa241f4870eba7d4f4becd99904725f2", "sources": ["arxiv", "semantic_scholar"], "title": "VAFL: a Method of Vertical Asynchronous Federated Learning", "abstract": "Horizontal Federated learning (FL) handles multi-client data that share the same set of features, and vertical FL trains a better predictor that combine all the features from different clients. This paper targets solving vertical FL in an asynchronous fashion, and develops a simple FL method. The new method allows each client to run stochastic gradient algorithms without coordination with other clients, so it is suitable for intermittent connectivity of clients. This method further uses a new technique of perturbed local embedding to ensure data privacy and improve communication efficiency. Theoretically, we present the convergence rate and privacy level of our method for strongly convex, nonconvex and even nonsmooth objectives separately. Empirically, we apply our method to FL on various image and healthcare datasets. The results compare favorably to centralized and synchronous FL methods.", "authors": ["Tianyi Chen", "Xiao Jin", "Yuejiao Sun", "Wotao Yin"], "categories": ["cs.LG", "cs.DC", "math.OC", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-07-12", "url": "https://arxiv.org/abs/2007.06081", "pdf_url": "https://arxiv.org/pdf/2007.06081v1", "arxiv_id": "2007.06081", "doi": null, "citation_count": 196, "influential_citation_count": 30, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.7457}
{"id": "402fd0ef93777de1c7c3bba82e9ad6404317604a7c0f45126942a45ba6585e25", "sources": ["arxiv", "semantic_scholar"], "title": "Client Adaptation improves Federated Learning with Simulated Non-IID Clients", "abstract": "We present a federated learning approach for learning a client adaptable, robust model when data is non-identically and non-independently distributed (non-IID) across clients. By simulating heterogeneous clients, we show that adding learned client-specific conditioning improves model performance, and the approach is shown to work on balanced and imbalanced data set from both audio and image domains. The client adaptation is implemented by a conditional gated activation unit and is particularly beneficial when there are large differences between the data distribution for each client, a common scenario in federated learning.", "authors": ["Laura Rieger", "Rasmus M. Th. Høegh", "Lars K. Hansen"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-07-09", "url": "https://arxiv.org/abs/2007.04806", "pdf_url": "https://arxiv.org/pdf/2007.04806v1", "arxiv_id": "2007.04806", "doi": null, "citation_count": 6, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "c48b0718891b22d4d8f6457dcaa87ad6f1f84faec819aa2d8c3d70733901fc1d", "sources": ["arxiv", "semantic_scholar"], "title": "BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning", "abstract": "Federated learning enables the development of a machine learning model among collaborating agents without requiring them to share their underlying data. However, malicious agents who train on random data, or worse, on datasets with the result classes inverted, can weaken the combined model. BlockFLow is an accountable federated learning system that is fully decentralized and privacy-preserving. Its primary goal is to reward agents proportional to the quality of their contribution while protecting the privacy of the underlying datasets and being resilient to malicious adversaries. Specifically, BlockFLow incorporates differential privacy, introduces a novel auditing mechanism for model contribution, and uses Ethereum smart contracts to incentivize good behavior. Unlike existing auditing and accountability methods for federated learning systems, our system does not require a centralized test dataset, sharing of datasets between the agents, or one or more trusted auditors; it is fully decentralized and resilient up to a 50% collusion attack in a malicious trust model. When run on the public Ethereum blockchain, BlockFLow uses the results from the audit to reward parties with cryptocurrency based on the quality of their contribution. We evaluated BlockFLow on two datasets that offer classification tasks solvable via logistic regression models. Our results show that the resultant auditing scores reflect the quality of the honest agents' datasets. Moreover, the scores from dishonest agents are statistically lower than those from the honest agents. These results, along with the reasonable blockchain costs, demonstrate the effectiveness of BlockFLow as an accountable federated learning system.", "authors": ["Vaikkunth Mugunthan", "Ravi Rahman", "Lalana Kagal"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-07-08", "url": "https://arxiv.org/abs/2007.03856", "pdf_url": "https://arxiv.org/pdf/2007.03856v1", "arxiv_id": "2007.03856", "doi": null, "citation_count": 46, "influential_citation_count": 6, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4225}
{"id": "14d4ff34a6f7c104294c52c8f7086a56ca1bbdd4a9a7741b4737e333f94adf9d", "sources": ["arxiv", "semantic_scholar"], "title": "Coded Computing for Federated Learning at the Edge", "abstract": "Federated Learning (FL) is an exciting new paradigm that enables training a global model from data generated locally at the client nodes, without moving client data to a centralized server. Performance of FL in a multi-access edge computing (MEC) network suffers from slow convergence due to heterogeneity and stochastic fluctuations in compute power and communication link qualities across clients. A recent work, Coded Federated Learning (CFL), proposes to mitigate stragglers and speed up training for linear regression tasks by assigning redundant computations at the MEC server. Coding redundancy in CFL is computed by exploiting statistical properties of compute and communication delays. We develop CodedFedL that addresses the difficult task of extending CFL to distributed non-linear regression and classification problems with multioutput labels. The key innovation of our work is to exploit distributed kernel embedding using random Fourier features that transforms the training task into distributed linear regression. We provide an analytical solution for load allocation, and demonstrate significant performance gains for CodedFedL through experiments over benchmark datasets using practical network parameters.", "authors": ["Saurav Prakash", "Sagar Dhakal", "Mustafa Akdeniz", "A. Salman Avestimehr", "Nageen Himayat"], "categories": ["cs.DC", "cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-07-07", "url": "https://arxiv.org/abs/2007.03273", "pdf_url": "https://arxiv.org/pdf/2007.03273v3", "arxiv_id": "2007.03273", "doi": null, "citation_count": 11, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.301}
{"id": "34b664b408e3c364c0fb314a660daeb5e687e69acfb9c1af2d75ae927cf8050a", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy Threats Against Federated Matrix Factorization", "abstract": "Matrix Factorization has been very successful in practical recommendation applications and e-commerce. Due to data shortage and stringent regulations, it can be hard to collect sufficient data to build performant recommender systems for a single company. Federated learning provides the possibility to bridge the data silos and build machine learning models without compromising privacy and security. Participants sharing common users or items collaboratively build a model over data from all the participants. There have been some works exploring the application of federated learning to recommender systems and the privacy issues in collaborative filtering systems. However, the privacy threats in federated matrix factorization are not studied. In this paper, we categorize federated matrix factorization into three types based on the partition of feature space and analyze privacy threats against each type of federated matrix factorization model. We also discuss privacy-preserving approaches. As far as we are aware, this is the first study of privacy threats of the matrix factorization method in the federated learning framework.", "authors": ["Dashan Gao", "Ben Tan", "Ce Ju", "Vincent W. Zheng", "Qiang Yang"], "categories": ["cs.CR", "cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-07-03", "url": "https://arxiv.org/abs/2007.01587", "pdf_url": "https://arxiv.org/pdf/2007.01587v1", "arxiv_id": "2007.01587", "doi": null, "citation_count": 14, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.294}
{"id": "9add605cdedc277e997669557c802793bd8fc114af1909f80daf1c6a2c24a669", "sources": ["arxiv", "semantic_scholar"], "title": "Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning", "abstract": "Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet wireless channels bring challenges for model training, in which channel randomness perturbs each worker's model update while multiple workers' updates incur significant interference under limited bandwidth. To address these challenges, in this work we formulate a novel constrained optimization problem, and propose an FL framework harnessing wireless channel perturbations and interference for improving privacy, bandwidth-efficiency, and scalability. The resultant algorithm is coined analog federated ADMM (A-FADMM) based on analog transmissions and the alternating direction method of multipliers (ADMM). In A-FADMM, all workers upload their model updates to the parameter server (PS) using a single channel via analog transmissions, during which all models are perturbed and aggregated over-the-air. This not only saves communication bandwidth, but also hides each worker's exact model update trajectory from any eavesdropper including the honest-but-curious PS, thereby preserving data privacy against model inversion attacks. We formally prove the convergence and privacy guarantees of A-FADMM for convex functions under time-varying channels, and numerically show the effectiveness of A-FADMM under noisy channels and stochastic non-convex functions, in terms of convergence speed and scalability, as well as communication bandwidth and energy efficiency.", "authors": ["Anis Elgabli", "Jihong Park", "Chaouki Ben Issaid", "Mehdi Bennis"], "categories": ["cs.LG", "cs.IT", "cs.NI", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-07-03", "url": "https://arxiv.org/abs/2007.01790", "pdf_url": "https://arxiv.org/pdf/2007.01790v2", "arxiv_id": "2007.01790", "doi": "10.1109/TCOMM.2021.3078783", "citation_count": 70, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Communications", "quality_score": 0.4628}
{"id": "009af31195d49cc02c6b9a436dd24d965412ff297d321f91bacb87830376c573", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy", "abstract": "The high demand of artificial intelligence services at the edges that also preserve data privacy has pushed the research on novel machine learning paradigms that fit those requirements. Federated learning has the ambition to protect data privacy through distributed learning methods that keep the data in their data silos. Likewise, differential privacy attains to improve the protection of data privacy by measuring the privacy loss in the communication among the elements of federated learning. The prospective matching of federated learning and differential privacy to the challenges of data privacy protection has caused the release of several software tools that support their functionalities, but they lack of the needed unified vision for those techniques, and a methodological workflow that support their use. Hence, we present the Sherpa.ai Federated Learning framework that is built upon an holistic view of federated learning and differential privacy. It results from the study of how to adapt the machine learning paradigm to federated learning, and the definition of methodological guidelines for developing artificial intelligence services based on federated learning and differential privacy. We show how to follow the methodological guidelines with the Sherpa.ai Federated Learning framework by means of a classification and a regression use cases.", "authors": ["Nuria Rodríguez-Barroso", "Goran Stipcich", "Daniel Jiménez-López", "José Antonio Ruiz-Millán", "Eugenio Martínez-Cámara", "Gerardo González-Seco", "M. Victoria Luzón", "Miguel Ángel Veganzones", "Francisco Herrera"], "categories": ["cs.LG", "cs.AI", "cs.CR", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-07-02", "url": "https://arxiv.org/abs/2007.00914", "pdf_url": "https://arxiv.org/pdf/2007.00914v2", "arxiv_id": "2007.00914", "doi": "10.1016/j.inffus.2020.07.009", "citation_count": 123, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Information Fusion", "quality_score": 0.5234}
{"id": "e0f0e5303b4e99efa29a9e02854ff59195e44718443ff6985bbd48bb5a8b54f6", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning", "abstract": "While existing federated learning approaches mostly require that clients have fully-labeled data to train on, in realistic settings, data obtained at the client-side often comes without any accompanying labels. Such deficiency of labels may result from either high labeling cost, or difficulty of annotation due to the requirement of expert knowledge. Thus the private data at each client may be either partly labeled, or completely unlabeled with labeled data being available only at the server, which leads us to a new practical federated learning problem, namely Federated Semi-Supervised Learning (FSSL). In this work, we study two essential scenarios of FSSL based on the location of the labeled data. The first scenario considers a conventional case where clients have both labeled and unlabeled data (labels-at-client), and the second scenario considers a more challenging case, where the labeled data is only available at the server (labels-at-server). We then propose a novel method to tackle the problems, which we refer to as Federated Matching (FedMatch). FedMatch improves upon naive combinations of federated learning and semi-supervised learning approaches with a new inter-client consistency loss and decomposition of the parameters for disjoint learning on labeled and unlabeled data. Through extensive experimental validation of our method in the two different scenarios, we show that our method outperforms both local semi-supervised learning and baselines which naively combine federated learning with semi-supervised learning. The code is available at https://github.com/wyjeong/FedMatch.", "authors": ["Wonyong Jeong", "Jaehong Yoon", "Eunho Yang", "Sung Ju Hwang"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-06-22", "url": "https://arxiv.org/abs/2006.12097", "pdf_url": "https://arxiv.org/pdf/2006.12097v3", "arxiv_id": "2006.12097", "doi": null, "citation_count": 280, "influential_citation_count": 58, "has_code": true, "code_url": "https://github.com/wyjeong/FedMatch", "venue": "International Conference on Learning Representations", "quality_score": 0.8854}
{"id": "95839a70948be8ec1d0d452c51e4dbd8b8ab9bfd75695c5214958e246182c526", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Survival Analysis with Discrete-Time Cox Models", "abstract": "Building machine learning models from decentralized datasets located in different centers with federated learning (FL) is a promising approach to circumvent local data scarcity while preserving privacy. However, the prominent Cox proportional hazards (PH) model, used for survival analysis, does not fit the FL framework, as its loss function is non-separable with respect to the samples. The naïve method to bypass this non-separability consists in calculating the losses per center, and minimizing their sum as an approximation of the true loss. We show that the resulting model may suffer from important performance loss in some adverse settings. Instead, we leverage the discrete-time extension of the Cox PH model to formulate survival analysis as a classification problem with a separable loss function. Using this approach, we train survival models using standard FL techniques on synthetic data, as well as real-world datasets from The Cancer Genome Atlas (TCGA), showing similar performance to a Cox PH model trained on aggregated data. Compared to previous works, the proposed method is more communication-efficient, more generic, and more amenable to using privacy-preserving techniques.", "authors": ["Mathieu Andreux", "Andre Manoel", "Romuald Menuet", "Charlie Saillard", "Chloé Simpson"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-06-16", "url": "https://arxiv.org/abs/2006.08997", "pdf_url": "https://arxiv.org/pdf/2006.08997v1", "arxiv_id": "2006.08997", "doi": null, "citation_count": 45, "influential_citation_count": 7, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4515}
{"id": "82338f6eb5ce9f622a45413d4a2b5f8aca7ca80f4e85bd74f3a58aa2e3156685", "sources": ["arxiv", "semantic_scholar"], "title": "Robust Federated Learning: The Case of Affine Distribution Shifts", "abstract": "Federated learning is a distributed paradigm that aims at training models using samples distributed across multiple users in a network while keeping the samples on users' devices with the aim of efficiency and protecting users privacy. In such settings, the training data is often statistically heterogeneous and manifests various distribution shifts across users, which degrades the performance of the learnt model. The primary goal of this paper is to develop a robust federated learning algorithm that achieves satisfactory performance against distribution shifts in users' samples. To achieve this goal, we first consider a structured affine distribution shift in users' data that captures the device-dependent data heterogeneity in federated settings. This perturbation model is applicable to various federated learning problems such as image classification where the images undergo device-dependent imperfections, e.g. different intensity, contrast, and brightness. To address affine distribution shifts across users, we propose a Federated Learning framework Robust to Affine distribution shifts (FLRA) that is provably robust against affine Wasserstein shifts to the distribution of observed samples. To solve the FLRA's distributed minimax problem, we propose a fast and efficient optimization method and provide convergence guarantees via a gradient Descent Ascent (GDA) method. We further prove generalization error bounds for the learnt classifier to show proper generalization from empirical distribution of samples to the true underlying distribution. We perform several numerical experiments to empirically support FLRA. We show that an affine distribution shift indeed suffices to significantly decrease the performance of the learnt classifier in a new test user, and our proposed algorithm achieves a significant gain in comparison to standard federated learning and adversarial training methods.", "authors": ["Amirhossein Reisizadeh", "Farzan Farnia", "Ramtin Pedarsani", "Ali Jadbabaie"], "categories": ["cs.LG", "math.OC", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-06-16", "url": "https://arxiv.org/abs/2006.08907", "pdf_url": "https://arxiv.org/pdf/2006.08907v1", "arxiv_id": "2006.08907", "doi": null, "citation_count": 189, "influential_citation_count": 18, "has_code": false, "code_url": null, "venue": "Neural Information Processing Systems", "quality_score": 0.6394}
{"id": "cf4913a5ea69f37a00c34f69e775980111404fac9cbc704553ac4056db2cbad4", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention", "abstract": "Prevention of stroke with its associated risk factors has been one of the public health priorities worldwide. Emerging artificial intelligence technology is being increasingly adopted to predict stroke. Because of privacy concerns, patient data are stored in distributed electronic health record (EHR) databases, voluminous clinical datasets, which prevent patient data from being aggregated and restrains AI technology to boost the accuracy of stroke prediction with centralized training data. In this work, our scientists and engineers propose a privacy-preserving scheme to predict the risk of stroke and deploy our federated prediction model on cloud servers. Our system of federated prediction model asynchronously supports any number of client connections and arbitrary local gradient iterations in each communication round. It adopts federated averaging during the model training process, without patient data being taken out of the hospitals during the whole process of model training and forecasting. With the privacy-preserving mechanism, our federated prediction model trains over all the healthcare data from hospitals in a certain city without actual data sharing among them. Therefore, it is not only secure but also more accurate than any single prediction model that trains over the data only from one single hospital. Especially for small hospitals with few confirmed stroke cases, our federated model boosts model performance by 10%~20% in several machine learning metrics. To help stroke experts comprehend the advantage of our prediction system more intuitively, we developed a mobile app that collects the key information of patients' statistics and demonstrates performance comparisons between the federated prediction model and the single prediction model during the federated training process.", "authors": ["Ce Ju", "Ruihui Zhao", "Jichao Sun", "Xiguang Wei", "Bo Zhao", "Yang Liu", "Hongshan Li", "Tianjian Chen", "Xinwei Zhang", "Dashan Gao", "Ben Tan", "Han Yu", "Chuning He", "Yuan Jin"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2020-06-15", "url": "https://arxiv.org/abs/2006.10517", "pdf_url": "https://arxiv.org/pdf/2006.10517v2", "arxiv_id": "2006.10517", "doi": null, "citation_count": 38, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3978}
{"id": "4228a26700baa5e3c50481b14f0ba874f04309109f141a065ed6f89e92d3c7ca", "sources": ["arxiv", "semantic_scholar"], "title": "Backdoor Attacks on Federated Meta-Learning", "abstract": "Federated learning allows multiple users to collaboratively train a shared classification model while preserving data privacy. This approach, where model updates are aggregated by a central server, was shown to be vulnerable to poisoning backdoor attacks: a malicious user can alter the shared model to arbitrarily classify specific inputs from a given class. In this paper, we analyze the effects of backdoor attacks on federated meta-learning, where users train a model that can be adapted to different sets of output classes using only a few examples. While the ability to adapt could, in principle, make federated learning frameworks more robust to backdoor attacks (when new training examples are benign), we find that even 1-shot~attacks can be very successful and persist after additional training. To address these vulnerabilities, we propose a defense mechanism inspired by matching networks, where the class of an input is predicted from the similarity of its features with a support set of labeled examples. By removing the decision logic from the model shared with the federation, success and persistence of backdoor attacks are greatly reduced.", "authors": ["Chien-Lun Chen", "Leana Golubchik", "Marco Paolieri"], "categories": ["cs.LG", "cs.CR", "cs.DC", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-06-12", "url": "https://arxiv.org/abs/2006.07026", "pdf_url": "https://arxiv.org/pdf/2006.07026v2", "arxiv_id": "2006.07026", "doi": null, "citation_count": 33, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3829}
{"id": "08e073dcc889cc10896feac84c66214bc8d10ab183c5007136c0e1f72b54b3f2", "sources": ["arxiv", "semantic_scholar"], "title": "XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning", "abstract": "User-generated data distributions are often imbalanced across devices and labels, hampering the performance of federated learning (FL). To remedy to this non-independent and identically distributed (non-IID) data problem, in this work we develop a privacy-preserving XOR based mixup data augmentation technique, coined XorMixup, and thereby propose a novel one-shot FL framework, termed XorMixFL. The core idea is to collect other devices' encoded data samples that are decoded only using each device's own data samples. The decoding provides synthetic-but-realistic samples until inducing an IID dataset, used for model training. Both encoding and decoding procedures follow the bit-wise XOR operations that intentionally distort raw samples, thereby preserving data privacy. Simulation results corroborate that XorMixFL achieves up to 17.6% higher accuracy than Vanilla FL under a non-IID MNIST dataset.", "authors": ["MyungJae Shin", "Chihoon Hwang", "Joongheon Kim", "Jihong Park", "Mehdi Bennis", "Seong-Lyun Kim"], "categories": ["cs.LG", "cs.CR", "eess.SP"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2020-06-09", "url": "https://arxiv.org/abs/2006.05148", "pdf_url": "https://arxiv.org/pdf/2006.05148v1", "arxiv_id": "2006.05148", "doi": null, "citation_count": 125, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5251}
{"id": "e33fa40509bb678e75c2edd95d283beeb47afc61e57476a2f164b803fdb392b8", "sources": ["arxiv", "semantic_scholar"], "title": "LDP-Fed: Federated Learning with Local Differential Privacy", "abstract": "This paper presents LDP-Fed, a novel federated learning system with a formal privacy guarantee using local differential privacy (LDP). Existing LDP protocols are developed primarily to ensure data privacy in the collection of single numerical or categorical values, such as click count in Web access logs. However, in federated learning model parameter updates are collected iteratively from each participant and consist of high dimensional, continuous values with high precision (10s of digits after the decimal point), making existing LDP protocols inapplicable. To address this challenge in LDP-Fed, we design and develop two novel approaches. First, LDP-Fed's LDP Module provides a formal differential privacy guarantee for the repeated collection of model training parameters in the federated training of large-scale neural networks over multiple individual participants' private datasets. Second, LDP-Fed implements a suite of selection and filtering techniques for perturbing and sharing select parameter updates with the parameter server. We validate our system deployed with a condensed LDP protocol in training deep neural networks on public data. We compare this version of LDP-Fed, coined CLDP-Fed, with other state-of-the-art approaches with respect to model accuracy, privacy preservation, and system capabilities.", "authors": ["Stacey Truex", "Ling Liu", "Ka-Ho Chow", "Mehmet Emre Gursoy", "Wenqi Wei"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-06-05", "url": "https://arxiv.org/abs/2006.03637", "pdf_url": "https://arxiv.org/pdf/2006.03637v1", "arxiv_id": "2006.03637", "doi": "10.1145/3378679.3394533", "citation_count": 523, "influential_citation_count": 34, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.772}
{"id": "63dbac2747b9dca4378d521c2897d9dbf4dc5e19a56f233dc0a103371bb9f196", "sources": ["arxiv", "semantic_scholar"], "title": "A Distributed Trust Framework for Privacy-Preserving Machine Learning", "abstract": "When training a machine learning model, it is standard procedure for the researcher to have full knowledge of both the data and model. However, this engenders a lack of trust between data owners and data scientists. Data owners are justifiably reluctant to relinquish control of private information to third parties. Privacy-preserving techniques distribute computation in order to ensure that data remains in the control of the owner while learning takes place. However, architectures distributed amongst multiple agents introduce an entirely new set of security and trust complications. These include data poisoning and model theft. This paper outlines a distributed infrastructure which is used to facilitate peer-to-peer trust between distributed agents; collaboratively performing a privacy-preserving workflow. Our outlined prototype sets industry gatekeepers and governance bodies as credential issuers. Before participating in the distributed learning workflow, malicious actors must first negotiate valid credentials. We detail a proof of concept using Hyperledger Aries, Decentralised Identifiers (DIDs) and Verifiable Credentials (VCs) to establish a distributed trust architecture during a privacy-preserving machine learning experiment. Specifically, we utilise secure and authenticated DID communication channels in order to facilitate a federated learning workflow related to mental health care data.", "authors": ["Will Abramson", "Adam James Hall", "Pavlos Papadopoulos", "Nikolaos Pitropakis", "William J Buchanan"], "categories": ["cs.CR", "cs.CY", "cs.DC", "cs.GT", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2020-06-03", "url": "https://arxiv.org/abs/2006.02456", "pdf_url": "https://arxiv.org/pdf/2006.02456v1", "arxiv_id": "2006.02456", "doi": "10.1007/978-3-030-58986-8_14", "citation_count": 24, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "Trust and Privacy in Digital Business", "quality_score": 0.3495}
{"id": "70f9655475708fba9e5305a0262bf50eea343bbb32e4c400fe5a061cc2193b92", "sources": ["arxiv", "semantic_scholar"], "title": "Scalable Privacy-Preserving Distributed Learning", "abstract": "In this paper, we address the problem of privacy-preserving distributed learning and the evaluation of machine-learning models by analyzing it in the widespread MapReduce abstraction that we extend with privacy constraints. We design SPINDLE (Scalable Privacy-preservINg Distributed LEarning), the first distributed and privacy-preserving system that covers the complete ML workflow by enabling the execution of a cooperative gradient-descent and the evaluation of the obtained model and by preserving data and model confidentiality in a passive-adversary model with up to N-1 colluding parties. SPINDLE uses multiparty homomorphic encryption to execute parallel high-depth computations on encrypted data without significant overhead. We instantiate SPINDLE for the training and evaluation of generalized linear models on distributed datasets and show that it is able to accurately (on par with non-secure centrally-trained models) and efficiently (due to a multi-level parallelization of the computations) train models that require a high number of iterations on large input data with thousands of features, distributed among hundreds of data providers. For instance, it trains a logistic-regression model on a dataset of one million samples with 32 features distributed among 160 data providers in less than three minutes.", "authors": ["David Froelicher", "Juan R. Troncoso-Pastoriza", "Apostolos Pyrgelis", "Sinem Sav", "Joao Sa Sousa", "Jean-Philippe Bossuat", "Jean-Pierre Hubaux"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2020-05-19", "url": "https://arxiv.org/abs/2005.09532", "pdf_url": "https://arxiv.org/pdf/2005.09532v5", "arxiv_id": "2005.09532", "doi": "10.2478/popets-2021-0030", "citation_count": 76, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "Proceedings on Privacy Enhancing Technologies", "quality_score": 0.4716}
{"id": "bdcf3c5ad7ed9ede08f3a6f541b2910517dcd38a940a8c7672129e3820a0e025", "sources": ["arxiv", "semantic_scholar"], "title": "Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning", "abstract": "Traditional distributed deep reinforcement learning (RL) commonly relies on exchanging the experience replay memory (RM) of each agent. Since the RM contains all state observations and action policy history, it may incur huge communication overhead while violating the privacy of each agent. Alternatively, this article presents a communication-efficient and privacy-preserving distributed RL framework, coined federated reinforcement distillation (FRD). In FRD, each agent exchanges its proxy experience replay memory (ProxRM), in which policies are locally averaged with respect to proxy states clustering actual states. To provide FRD design insights, we present ablation studies on the impact of ProxRM structures, neural network architectures, and communication intervals. Furthermore, we propose an improved version of FRD, coined mixup augmented FRD (MixFRD), in which ProxRM is interpolated using the mixup data augmentation algorithm. Simulations in a Cartpole environment validate the effectiveness of MixFRD in reducing the variance of mission completion time and communication cost, compared to the benchmark schemes, vanilla FRD, federated reinforcement learning (FRL), and policy distillation (PD).", "authors": ["Han Cha", "Jihong Park", "Hyesung Kim", "Mehdi Bennis", "Seong-Lyun Kim"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2020-05-13", "url": "https://arxiv.org/abs/2005.06105", "pdf_url": "https://arxiv.org/pdf/2005.06105v2", "arxiv_id": "2005.06105", "doi": "10.1109/MIS.2020.2994942", "citation_count": 33, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "IEEE Intelligent Systems", "quality_score": 0.3829}
{"id": "abac990f52276d4197d4e4599c69f4f76b441b35fe0ce8cb72d8bc26d0f6cf62", "sources": ["arxiv", "semantic_scholar"], "title": "Information-Theoretic Bounds on the Generalization Error and Privacy Leakage in Federated Learning", "abstract": "Machine learning algorithms operating on mobile networks can be characterized into three different categories. First is the classical situation in which the end-user devices send their data to a central server where this data is used to train a model. Second is the distributed setting in which each device trains its own model and send its model parameters to a central server where these model parameters are aggregated to create one final model. Third is the federated learning setting in which, at any given time $t$, a certain number of active end users train with their own local data along with feedback provided by the central server and then send their newly estimated model parameters to the central server. The server, then, aggregates these new parameters, updates its own model, and feeds the updated parameters back to all the end users, continuing this process until it converges. The main objective of this work is to provide an information-theoretic framework for all of the aforementioned learning paradigms. Moreover, using the provided framework, we develop upper and lower bounds on the generalization error together with bounds on the privacy leakage in the classical, distributed and federated learning settings. Keywords: Federated Learning, Distributed Learning, Machine Learning, Model Aggregation.", "authors": ["Semih Yagli", "Alex Dytso", "H. Vincent Poor"], "categories": ["cs.LG", "cs.CR", "cs.DC", "cs.NE", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-05-05", "url": "https://arxiv.org/abs/2005.02503", "pdf_url": "https://arxiv.org/pdf/2005.02503v1", "arxiv_id": "2005.02503", "doi": "10.1109/spawc48557.2020.9154277", "citation_count": 37, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "International Workshop on Signal Processing Advances in Wireless Communications", "quality_score": 0.3949}
{"id": "63590a75231f064d5fc722fc592aba63421e184ce04fa9ee770e7032923a06b9", "sources": ["arxiv", "semantic_scholar"], "title": "Private Dataset Generation Using Privacy Preserving Collaborative Learning", "abstract": "With increasing usage of deep learning algorithms in many application, new research questions related to privacy and adversarial attacks are emerging. However, the deep learning algorithm improvement needs more and more data to be shared within research community. Methodologies like federated learning, differential privacy, additive secret sharing provides a way to train machine learning models on edge without moving the data from the edge. However, it is very computationally intensive and prone to adversarial attacks. Therefore, this work introduces a privacy preserving FedCollabNN framework for training machine learning models at edge, which is computationally efficient and robust against adversarial attacks. The simulation results using MNIST dataset indicates the effectiveness of the framework.", "authors": ["Amit Chaulwar"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-04-28", "url": "https://arxiv.org/abs/2004.13598", "pdf_url": "https://arxiv.org/pdf/2004.13598v1", "arxiv_id": "2004.13598", "doi": null, "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "3efd08ecb76222f3c31ddaa4b4af9791f7065ecabae51a26e9969ce864b8c002", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Transfer Learning for EEG Signal Classification", "abstract": "The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for classification of electroencephalographic (EEG) recordings has been restricted by the lack of large datasets. Privacy concerns associated with EEG signals limit the possibility of constructing a large EEG-BCI dataset by the conglomeration of multiple small ones for jointly training machine learning models. Hence, in this paper, we propose a novel privacy-preserving DL architecture named federated transfer learning (FTL) for EEG classification that is based on the federated learning framework. Working with the single-trial covariance matrix, the proposed architecture extracts common discriminative information from multi-subject EEG data with the help of domain adaptation techniques. We evaluate the performance of the proposed architecture on the PhysioNet dataset for 2-class motor imagery classification. While avoiding the actual data sharing, our FTL approach achieves 2% higher classification accuracy in a subject-adaptive analysis. Also, in the absence of multi-subject data, our architecture provides 6% better accuracy compared to other state-of-the-art DL architectures.", "authors": ["Ce Ju", "Dashan Gao", "Ravikiran Mane", "Ben Tan", "Yang Liu", "Cuntai Guan"], "categories": ["cs.LG", "eess.SP"], "fields_of_study": ["Computer Science", "Medicine", "Engineering"], "published_date": "2020-04-26", "url": "https://arxiv.org/abs/2004.12321", "pdf_url": "https://arxiv.org/pdf/2004.12321v5", "arxiv_id": "2004.12321", "doi": "10.1109/EMBC44109.2020.9175344", "citation_count": 129, "influential_citation_count": 6, "has_code": true, "code_url": "https://github.com/DashanGao/Federated-Transfer-Leraning-for-EEG", "venue": "Annual International Conference of the IEEE Engineering in Medicine and Biology Society", "quality_score": 0.5285}
{"id": "d5e7b0cf1c28466e1d19d154e41ff9d7762da8733205a5ed0b921b0fb9702548", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy Preserving Distributed Machine Learning with Federated Learning", "abstract": "Edge computing and distributed machine learning have advanced to a level that can revolutionize a particular organization. Distributed devices such as the Internet of Things (IoT) often produce a large amount of data, eventually resulting in big data that can be vital in uncovering hidden patterns, and other insights in numerous fields such as healthcare, banking, and policing. Data related to areas such as healthcare and banking can contain potentially sensitive data that can become public if they are not appropriately sanitized. Federated learning (FedML) is a recently developed distributed machine learning (DML) approach that tries to preserve privacy by bringing the learning of an ML model to data owners'. However, literature shows different attack methods such as membership inference that exploit the vulnerabilities of ML models as well as the coordinating servers to retrieve private data. Hence, FedML needs additional measures to guarantee data privacy. Furthermore, big data often requires more resources than available in a standard computer. This paper addresses these issues by proposing a distributed perturbation algorithm named as DISTPAB, for privacy preservation of horizontally partitioned data. DISTPAB alleviates computational bottlenecks by distributing the task of privacy preservation utilizing the asymmetry of resources of a distributed environment, which can have resource-constrained devices as well as high-performance computers. Experiments show that DISTPAB provides high accuracy, high efficiency, high scalability, and high attack resistance. Further experiments on privacy-preserving FedML show that DISTPAB is an excellent solution to stop privacy leaks in DML while preserving high data utility.", "authors": ["M. A. P. Chamikara", "P. Bertok", "I. Khalil", "D. Liu", "S. Camtepe"], "categories": ["cs.DB"], "fields_of_study": ["Computer Science"], "published_date": "2020-04-25", "url": "https://arxiv.org/abs/2004.12108", "pdf_url": "https://arxiv.org/pdf/2004.12108v2", "arxiv_id": "2004.12108", "doi": "10.1016/J.COMCOM.2021.02.014", "citation_count": 125, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "Computer Communications", "quality_score": 0.5251}
{"id": "a9db408e06531ad286f4ce95f2718c0d05c06aff201f7cb4f3157b90f0fd1682", "sources": ["arxiv", "semantic_scholar"], "title": "A Review of Privacy-preserving Federated Learning for the Internet-of-Things", "abstract": "The Internet-of-Things (IoT) generates vast quantities of data, much of it attributable to individuals' activity and behaviour. Gathering personal data and performing machine learning tasks on this data in a central location presents a significant privacy risk to individuals as well as challenges with communicating this data to the cloud. However, analytics based on machine learning and in particular deep learning benefit greatly from large amounts of data to develop high-performance predictive models. This work reviews federated learning as an approach for performing machine learning on distributed data with the goal of protecting the privacy of user-generated data as well as reducing communication costs associated with data transfer. We survey a wide variety of papers covering communication-efficiency, client heterogeneity and privacy preserving methods that are crucial for federated learning in the context of the IoT. Throughout this review, we identify the strengths and weaknesses of different methods applied to federated learning and finally, we outline future directions for privacy preserving federated learning research, particularly focusing on IoT applications.", "authors": ["Christopher Briggs", "Zhong Fan", "Peter Andras"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-04-24", "url": "https://arxiv.org/abs/2004.11794", "pdf_url": "https://arxiv.org/pdf/2004.11794v2", "arxiv_id": "2004.11794", "doi": null, "citation_count": 15, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.301}
{"id": "0da120d2eef853104997559bafff31b6daa23a585a2109e3d2ba4527b3ae2dab", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search", "abstract": "Federated Learning (FL) has been proved to be an effective learning framework when data cannot be centralized due to privacy, communication costs, and regulatory restrictions. When training deep learning models under an FL setting, people employ the predefined model architecture discovered in the centralized environment. However, this predefined architecture may not be the optimal choice because it may not fit data with non-identical and independent distribution (non-IID). Thus, we advocate automating federated learning (AutoFL) to improve model accuracy and reduce the manual design effort. We specifically study AutoFL via Neural Architecture Search (NAS), which can automate the design process. We propose a Federated NAS (FedNAS) algorithm to help scattered workers collaboratively searching for a better architecture with higher accuracy. We also build a system based on FedNAS. Our experiments on non-IID dataset show that the architecture searched by FedNAS can outperform the manually predefined architecture.", "authors": ["Chaoyang He", "Murali Annavaram", "Salman Avestimehr"], "categories": ["cs.LG", "cs.CV", "cs.DC", "cs.MA", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-04-18", "url": "https://arxiv.org/abs/2004.08546", "pdf_url": "https://arxiv.org/pdf/2004.08546v4", "arxiv_id": "2004.08546", "doi": null, "citation_count": 96, "influential_citation_count": 13, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5731}
{"id": "b31ab88240ffc692875649f1995cc483affb3380eb3cc346ac4220069c80ff89", "sources": ["arxiv", "semantic_scholar"], "title": "PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networks", "abstract": "Federated Learning (FL) enables a large number of users to jointly learn a shared machine learning (ML) model, coordinated by a centralized server, where the data is distributed across multiple devices. This approach enables the server or users to train and learn an ML model using gradient descent, while keeping all the training data on users' devices. We consider training an ML model over a mobile network where user dropout is a common phenomenon. Although federated learning was aimed at reducing data privacy risks, the ML model privacy has not received much attention. In this work, we present PrivFL, a privacy-preserving system for training (predictive) linear and logistic regression models and oblivious predictions in the federated setting, while guaranteeing data and model privacy as well as ensuring robustness to users dropping out in the network. We design two privacy-preserving protocols for training linear and logistic regression models based on an additive homomorphic encryption (HE) scheme and an aggregation protocol. Exploiting the training algorithm of federated learning, at the core of our training protocols is a secure multiparty global gradient computation on alive users' data. We analyze the security of our training protocols against semi-honest adversaries. As long as the aggregation protocol is secure under the aggregation privacy game and the additive HE scheme is semantically secure, PrivFL guarantees the users' data privacy against the server, and the server's regression model privacy against the users. We demonstrate the performance of PrivFL on real-world datasets and show its applicability in the federated learning system.", "authors": ["Kalikinkar Mandal", "Guang Gong"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2020-04-05", "url": "https://arxiv.org/abs/2004.02264", "pdf_url": "https://arxiv.org/pdf/2004.02264v1", "arxiv_id": "2004.02264", "doi": "10.1145/3338466.3358926", "citation_count": 91, "influential_citation_count": 8, "has_code": false, "code_url": null, "venue": "IACR Cryptology ePrint Archive", "quality_score": 0.4909}
{"id": "2cab91132f5204cd88696d1b923e0b02bd41083092a20db455a32d7023ee5875", "sources": ["arxiv", "semantic_scholar"], "title": "Concentrated Differentially Private and Utility Preserving Federated Learning", "abstract": "Federated learning is a machine learning setting where a set of edge devices collaboratively train a model under the orchestration of a central server without sharing their local data. At each communication round of federated learning, edge devices perform multiple steps of stochastic gradient descent with their local data and then upload the computation results to the server for model update. During this process, the challenge of privacy leakage arises due to the information exchange between edge devices and the server when the server is not fully trusted. While some previous privacy-preserving mechanisms could readily be used for federated learning, they usually come at a high cost on convergence of the algorithm and utility of the learned model. In this paper, we develop a federated learning approach that addresses the privacy challenge without much degradation on model utility through a combination of local gradient perturbation, secure aggregation, and zero-concentrated differential privacy (zCDP). We provide a tight end-to-end privacy guarantee of our approach and analyze its theoretical convergence rates. Through extensive numerical experiments on real-world datasets, we demonstrate the effectiveness of our proposed method and show its superior trade-off between privacy and model utility.", "authors": ["Rui Hu", "Yuanxiong Guo", "Yanmin Gong"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-03-30", "url": "https://arxiv.org/abs/2003.13761", "pdf_url": "https://arxiv.org/pdf/2003.13761v4", "arxiv_id": "2003.13761", "doi": null, "citation_count": 13, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2865}
{"id": "76ea69d344bdb9c322163dadb781845607888220c185d6bb87628b92ff0ae288", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach", "abstract": "Existing traffic flow forecasting approaches by deep learning models achieve excellent success based on a large volume of datasets gathered by governments and organizations. However, these datasets may contain lots of user's private data, which is challenging the current prediction approaches as user privacy is calling for the public concern in recent years. Therefore, how to develop accurate traffic prediction while preserving privacy is a significant problem to be solved, and there is a trade-off between these two objectives. To address this challenge, we introduce a privacy-preserving machine learning technique named federated learning and propose a Federated Learning-based Gated Recurrent Unit neural network algorithm (FedGRU) for traffic flow prediction. FedGRU differs from current centralized learning methods and updates universal learning models through a secure parameter aggregation mechanism rather than directly sharing raw data among organizations. In the secure parameter aggregation mechanism, we adopt a Federated Averaging algorithm to reduce the communication overhead during the model parameter transmission process. Furthermore, we design a Joint Announcement Protocol to improve the scalability of FedGRU. We also propose an ensemble clustering-based scheme for traffic flow prediction by grouping the organizations into clusters before applying FedGRU algorithm. Through extensive case studies on a real-world dataset, it is shown that FedGRU's prediction accuracy is 90.96% higher than the advanced deep learning models, which confirm that FedGRU can achieve accurate and timely traffic prediction without compromising the privacy and security of raw data.", "authors": ["Yi Liu", "James J. Q. Yu", "Jiawen Kang", "Dusit Niyato", "Shuyu Zhang"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-03-19", "url": "https://arxiv.org/abs/2003.08725", "pdf_url": "https://arxiv.org/pdf/2003.08725v1", "arxiv_id": "2003.08725", "doi": "10.1109/JIOT.2020.2991401", "citation_count": 543, "influential_citation_count": 22, "has_code": false, "code_url": null, "venue": "IEEE Internet of Things Journal", "quality_score": 0.6839}
{"id": "fcf3b960d75721f67997e40c89850b09fa35b314c0e1c3dfa1903652eeba8535", "sources": ["arxiv", "semantic_scholar"], "title": "A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems", "abstract": "Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices, each with its own local training data set. In this paper, we present a compressive sensing approach for federated learning over massive multiple-input multiple-output communication systems in which the central server equipped with a massive antenna array communicates with the wireless devices. One major challenge in system design is to reconstruct local gradient vectors accurately at the central server, which are computed-and-sent from the wireless devices. To overcome this challenge, we first establish a transmission strategy to construct sparse transmitted signals from the local gradient vectors at the devices. We then propose a compressive sensing algorithm enabling the server to iteratively find the linear minimum-mean-square-error (LMMSE) estimate of the transmitted signal by exploiting its sparsity. We also derive an analytical threshold for the residual error at each iteration, to design the stopping criterion of the proposed algorithm. We show that for a sparse transmitted signal, the proposed algorithm requires less computationally complexity than LMMSE. Simulation results demonstrate that the presented approach outperforms conventional linear beamforming approaches and reduces the performance gap between federated learning and centralized learning with perfect reconstruction.", "authors": ["Yo-Seb Jeon", "Mohammad Mohammadi Amiri", "Jun Li", "H. Vincent Poor"], "categories": ["eess.SP", "cs.DC", "cs.LG"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2020-03-18", "url": "https://arxiv.org/abs/2003.08059", "pdf_url": "https://arxiv.org/pdf/2003.08059v2", "arxiv_id": "2003.08059", "doi": null, "citation_count": 10, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2603}
{"id": "695990104e87c040459cc88bfa4ff9524103543003345061cd087560ddd586dc", "sources": ["arxiv", "semantic_scholar"], "title": "Differential Privacy at Risk: Bridging Randomness and Privacy Budget", "abstract": "The calibration of noise for a privacy-preserving mechanism depends on the sensitivity of the query and the prescribed privacy level. A data steward must make the non-trivial choice of a privacy level that balances the requirements of users and the monetary constraints of the business entity. We analyse roles of the sources of randomness, namely the explicit randomness induced by the noise distribution and the implicit randomness induced by the data-generation distribution, that are involved in the design of a privacy-preserving mechanism. The finer analysis enables us to provide stronger privacy guarantees with quantifiable risks. Thus, we propose privacy at risk that is a probabilistic calibration of privacy-preserving mechanisms. We provide a composition theorem that leverages privacy at risk. We instantiate the probabilistic calibration for the Laplace mechanism by providing analytical results. We also propose a cost model that bridges the gap between the privacy level and the compensation budget estimated by a GDPR compliant business entity. The convexity of the proposed cost model leads to a unique fine-tuning of privacy level that minimises the compensation budget. We show its effectiveness by illustrating a realistic scenario that avoids overestimation of the compensation budget by using privacy at risk for the Laplace mechanism. We quantitatively show that composition using the cost optimal privacy at risk provides stronger privacy guarantee than the classical advanced composition.", "authors": ["Ashish Dandekar", "Debabrota Basu", "Stephane Bressan"], "categories": ["cs.CR", "cs.DS", "cs.LG", "math.ST", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-03-02", "url": "https://arxiv.org/abs/2003.00973", "pdf_url": "https://arxiv.org/pdf/2003.00973v2", "arxiv_id": "2003.00973", "doi": "10.2478/POPETS-2021-0005", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Proceedings on Privacy Enhancing Technologies", "quality_score": 0.25}
{"id": "3c1665179baba5527d90146bec4c9877200ed3628e479ba146d6b444d014afb1", "sources": ["arxiv", "semantic_scholar"], "title": "User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization", "abstract": "Federated learning (FL), as a type of collaborative machine learning framework, is capable of preserving private data from mobile terminals (MTs) while training the data into useful models. Nevertheless, from a viewpoint of information theory, it is still possible for a curious server to infer private information from the shared models uploaded by MTs. To address this problem, we first make use of the concept of local differential privacy (LDP), and propose a user-level differential privacy (UDP) algorithm by adding artificial noise to the shared models before uploading them to servers. According to our analysis, the UDP framework can realize $(ε_{i}, δ_{i})$-LDP for the $i$-th MT with adjustable privacy protection levels by varying the variances of the artificial noise processes. We then derive a theoretical convergence upper-bound for the UDP algorithm. It reveals that there exists an optimal number of communication rounds to achieve the best learning performance. More importantly, we propose a communication rounds discounting (CRD) method. Compared with the heuristic search method, the proposed CRD method can achieve a much better trade-off between the computational complexity of searching and the convergence performance. Extensive experiments indicate that our UDP algorithm using the proposed CRD method can effectively improve both the training efficiency and model quality for the given privacy protection levels.", "authors": ["Kang Wei", "Jun Li", "Ming Ding", "Chuan Ma", "Hang Su", "Bo Zhang", "H. Vincent Poor"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2020-02-29", "url": "https://arxiv.org/abs/2003.00229", "pdf_url": "https://arxiv.org/pdf/2003.00229v2", "arxiv_id": "2003.00229", "doi": null, "citation_count": 12, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2785}
{"id": "b23620941d994b7f1a376f66816293fe569bde12a8a0e2c094db9b3931c6064d", "sources": ["arxiv", "semantic_scholar"], "title": "Anonymizing Data for Privacy-Preserving Federated Learning", "abstract": "Federated learning enables training a global machine learning model from data distributed across multiple sites, without having to move the data. This is particularly relevant in healthcare applications, where data is rife with personal, highly-sensitive information, and data analysis methods must provably comply with regulatory guidelines. Although federated learning prevents sharing raw data, it is still possible to launch privacy attacks on the model parameters that are exposed during the training process, or on the generated machine learning model. In this paper, we propose the first syntactic approach for offering privacy in the context of federated learning. Unlike the state-of-the-art differential privacy-based frameworks, our approach aims to maximize utility or model performance, while supporting a defensible level of privacy, as demanded by GDPR and HIPAA. We perform a comprehensive empirical evaluation on two important problems in the healthcare domain, using real-world electronic health data of 1 million patients. The results demonstrate the effectiveness of our approach in achieving high model performance, while offering the desired level of privacy. Through comparative studies, we also show that, for varying datasets, experimental setups, and privacy budgets, our approach offers higher model performance than differential privacy-based techniques in federated learning.", "authors": ["Olivia Choudhury", "Aris Gkoulalas-Divanis", "Theodoros Salonidis", "Issa Sylla", "Yoonyoung Park", "Grace Hsu", "Amar Das"], "categories": ["cs.CR", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2020-02-21", "url": "https://arxiv.org/abs/2002.09096", "pdf_url": "https://arxiv.org/pdf/2002.09096v1", "arxiv_id": "2002.09096", "doi": null, "citation_count": 62, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4498}
{"id": "5efe61e40ea8765ce7ef8fde9798540d1ae60e807dc88ed17d2bb4bd5ec82f95", "sources": ["arxiv", "semantic_scholar"], "title": "PrivacyFL: A simulator for privacy-preserving and secure federated learning", "abstract": "Federated learning is a technique that enables distributed clients to collaboratively learn a shared machine learning model while keeping their training data localized. This reduces data privacy risks, however, privacy concerns still exist since it is possible to leak information about the training dataset from the trained model's weights or parameters. Setting up a federated learning environment, especially with security and privacy guarantees, is a time-consuming process with numerous configurations and parameters that can be manipulated. In order to help clients ensure that collaboration is feasible and to check that it improves their model accuracy, a real-world simulator for privacy-preserving and secure federated learning is required. In this paper, we introduce PrivacyFL, which is an extensible, easily configurable and scalable simulator for federated learning environments. Its key features include latency simulation, robustness to client departure, support for both centralized and decentralized learning, and configurable privacy and security mechanisms based on differential privacy and secure multiparty computation. In this paper, we motivate our research, describe the architecture of the simulator and associated protocols, and discuss its evaluation in numerous scenarios that highlight its wide range of functionality and its advantages. Our paper addresses a significant real-world problem: checking the feasibility of participating in a federated learning environment under a variety of circumstances. It also has a strong practical impact because organizations such as hospitals, banks, and research institutes, which have large amounts of sensitive data and would like to collaborate, would greatly benefit from having a system that enables them to do so in a privacy-preserving and secure manner.", "authors": ["Vaikkunth Mugunthan", "Anton Peraire-Bueno", "Lalana Kagal"], "categories": ["cs.LG", "cs.CR", "cs.DC", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-02-19", "url": "https://arxiv.org/abs/2002.08423", "pdf_url": "https://arxiv.org/pdf/2002.08423v2", "arxiv_id": "2002.08423", "doi": "10.1145/3340531.3412771", "citation_count": 68, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "International Conference on Information and Knowledge Management", "quality_score": 0.4597}
{"id": "976b259bbe087987ad0c0b01638b0439b6be72cd87f6f29d50cfe173629dad6c", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Extra-Trees with Privacy Preserving", "abstract": "It is commonly observed that the data are scattered everywhere and difficult to be centralized. The data privacy and security also become a sensitive topic. The laws and regulations such as the European Union's General Data Protection Regulation (GDPR) are designed to protect the public's data privacy. However, machine learning requires a large amount of data for better performance, and the current circumstances put deploying real-life AI applications in an extremely difficult situation. To tackle these challenges, in this paper we propose a novel privacy-preserving federated machine learning model, named Federated Extra-Trees, which applies local differential privacy in the federated trees model. A secure multi-institutional machine learning system was developed to provide superior performance by processing the modeling jointly on different clients without exchanging any raw data. We have validated the accuracy of our work by conducting extensive experiments on public datasets and the efficiency and robustness were also verified by simulating the real-world scenarios. Overall, we presented an extensible, scalable and practical solution to handle the data island problem.", "authors": ["Yang Liu", "Mingxin Chen", "Wenxi Zhang", "Junbo Zhang", "Yu Zheng"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-02-18", "url": "https://arxiv.org/abs/2002.07323", "pdf_url": "https://arxiv.org/pdf/2002.07323v1", "arxiv_id": "2002.07323", "doi": null, "citation_count": 3, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.301}
{"id": "649832ad36c420fdb4b3718d8d92d8c25116413fe56ba9ec630db5e9d44d0f8b", "sources": ["arxiv", "semantic_scholar"], "title": "Prophet: Proactive Candidate-Selection for Federated Learning by Predicting the Qualities of Training and Reporting Phases", "abstract": "Although the challenge of the device connection is much relieved in 5G networks, the training latency is still an obstacle preventing Federated Learning (FL) from being largely adopted. One of the most fundamental problems that lead to large latency is the bad candidate-selection for FL. In the dynamic environment, the mobile devices selected by the existing reactive candidate-selection algorithms very possibly fail to complete the training and reporting phases of FL, because the FL parameter server only knows the currently-observed resources of all candidates. To this end, we study the proactive candidate-selection for FL in this paper. We first let each candidate device predict the qualities of both its training and reporting phases locally using LSTM. Then, the proposed candidateselection algorithm is implemented by the Deep Reinforcement Learning (DRL) framework. Finally, the real-world trace-driven experiments prove that the proposed approach outperforms the existing reactive algorithms", "authors": ["Huawei Huang", "Kangying Lin", "Song Guo", "Pan Zhou", "Zibin Zheng"], "categories": ["cs.LG", "cs.DC", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-02-03", "url": "https://arxiv.org/abs/2002.00577", "pdf_url": "https://arxiv.org/pdf/2002.00577v2", "arxiv_id": "2002.00577", "doi": null, "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2258}
{"id": "85418827696fba7085e6498caf76610637d6c7ffebbd3069072b69f1a38102f8", "sources": ["arxiv", "semantic_scholar"], "title": "A Federated Deep Learning Framework for Privacy Preservation and Communication Efficiency", "abstract": "Deep learning has achieved great success in many applications. However, its deployment in practice has been hurdled by two issues: the privacy of data that has to be aggregated centrally for model training and high communication overhead due to transmission of a large amount of data usually geographically distributed. Addressing both issues is challenging and most existing works could not provide an efficient solution. In this paper, we develop FedPC, a Federated Deep Learning Framework for Privacy Preservation and Communication Efficiency. The framework allows a model to be learned on multiple private datasets while not revealing any information of training data, even with intermediate data. The framework also minimizes the amount of data exchanged to update the model. We formally prove the convergence of the learning model when training with FedPC and its privacy-preserving property. We perform extensive experiments to evaluate the performance of FedPC in terms of the approximation to the upper-bound performance (when training centrally) and communication overhead. The results show that FedPC maintains the performance approximation of the models within $8.5\\%$ of the centrally-trained models when data is distributed to 10 computing nodes. FedPC also reduces the communication overhead by up to $42.20\\%$ compared to existing works.", "authors": ["Tien-Dung Cao", "Tram Truong-Huu", "Hien Tran", "Khanh Tran"], "categories": ["cs.DC", "cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2020-01-22", "url": "https://arxiv.org/abs/2001.09782", "pdf_url": "https://arxiv.org/pdf/2001.09782v3", "arxiv_id": "2001.09782", "doi": "10.1016/j.sysarc.2022.102413", "citation_count": 30, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal of systems architecture", "quality_score": 0.3728}
{"id": "a27b76844521a1c8fb24fe06a714b99e0679da96c35e9d37a9d0cf881de017ca", "sources": ["arxiv", "semantic_scholar"], "title": "Stratified cross-validation for unbiased and privacy-preserving federated learning", "abstract": "Large-scale collections of electronic records constitute both an opportunity for the development of more accurate prediction models and a threat for privacy. To limit privacy exposure new privacy-enhancing techniques are emerging such as federated learning which enables large-scale data analysis while avoiding the centralization of records in a unique database that would represent a critical point of failure. Although promising regarding privacy protection, federated learning prevents using some data-cleaning algorithms thus inducing new biases. In this work we focus on the recurrent problem of duplicated records that, if not handled properly, may cause over-optimistic estimations of a model's performances. We introduce and discuss stratified cross-validation, a validation methodology that leverages stratification techniques to prevent data leakage in federated learning settings without relying on demanding deduplication algorithms.", "authors": ["R. Bey", "R. Goussault", "M. Benchoufi", "R. Porcher"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-01-22", "url": "https://arxiv.org/abs/2001.08090", "pdf_url": "https://arxiv.org/pdf/2001.08090v2", "arxiv_id": "2001.08090", "doi": null, "citation_count": 10, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2603}
{"id": "cbfe95983761187da18a1931555366127c583f3730a8fb0210ee5ed4b6c26662", "sources": ["arxiv", "semantic_scholar"], "title": "Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results", "abstract": "Deep learning models have shown their advantage in many different tasks, including neuroimage analysis. However, to effectively train a high-quality deep learning model, the aggregation of a significant amount of patient information is required. The time and cost for acquisition and annotation in assembling, for example, large fMRI datasets make it difficult to acquire large numbers at a single site. However, due to the need to protect the privacy of patient data, it is hard to assemble a central database from multiple institutions. Federated learning allows for population-level models to be trained without centralizing entities' data by transmitting the global model to local entities, training the model locally, and then averaging the gradients or weights in the global model. However, some studies suggest that private information can be recovered from the model gradients or weights. In this work, we address the problem of multi-site fMRI classification with a privacy-preserving strategy. To solve the problem, we propose a federated learning approach, where a decentralized iterative optimization algorithm is implemented and shared local model weights are altered by a randomization mechanism. Considering the systemic differences of fMRI distributions from different sites, we further propose two domain adaptation methods in this federated learning formulation. We investigate various practical aspects of federated model optimization and compare federated learning with alternative training strategies. Overall, our results demonstrate that it is promising to utilize multi-site data without data sharing to boost neuroimage analysis performance and find reliable disease-related biomarkers. Our proposed pipeline can be generalized to other privacy-sensitive medical data analysis problems.", "authors": ["Xiaoxiao Li", "Yufeng Gu", "Nicha Dvornek", "Lawrence Staib", "Pamela Ventola", "James S. Duncan"], "categories": ["cs.LG", "eess.IV"], "fields_of_study": ["Computer Science", "Medicine", "Engineering"], "published_date": "2020-01-16", "url": "https://arxiv.org/abs/2001.05647", "pdf_url": "https://arxiv.org/pdf/2001.05647v3", "arxiv_id": "2001.05647", "doi": "10.1016/j.media.2020.101765", "citation_count": 457, "influential_citation_count": 29, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.7386}
{"id": "3fc08e99bbe8a39862bc1afa75f48f0bc295349489c746d46ebc63551d7c7986", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Method", "abstract": "Received Signal Strength (RSS) fingerprint-based localization has attracted a lot of research effort and cultivated many commercial applications of location-based services due to its low cost and ease of implementation. Many studies are exploring the use of deep learning (DL) algorithms for localization. DL's ability to extract features and to classify autonomously makes it an attractive solution for fingerprint-based localization. These solutions require frequent retraining of DL models with vast amounts of measurements. Although crowdsourcing is an excellent way to gather immense amounts of data, it jeopardizes the privacy of participants, as it requires to collect labeled data at a centralized server. Recently, federated learning has emerged as a practical concept in solving the privacy preservation issue of crowdsourcing participants by performing model training at the edge devices in a decentralized manner; the participants do not expose their data anymore to a centralized server. This paper presents a novel method utilizing federated learning to improve the accuracy of RSS fingerprint-based localization while preserving the privacy of the crowdsourcing participants. Employing federated learning allows ensuring \\emph{preserving the privacy of user data} while enabling an adequate localization performance with experimental data captured in real-world settings. The proposed method improved localization accuracy by 1.8 meters when used as a booster for centralized learning and achieved satisfactory localization accuracy when used standalone.", "authors": ["Bekir Sait Ciftler", "Abdullatif Albaseer", "Noureddine Lasla", "Mohamed Abdallah"], "categories": ["cs.NI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2020-01-07", "url": "https://arxiv.org/abs/2001.01911", "pdf_url": "https://arxiv.org/pdf/2001.01911v2", "arxiv_id": "2001.01911", "doi": null, "citation_count": 28, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3656}
{"id": "681eae8777d6974237f80c6d75f72d68055aca7f52b2d4b42b21d90d4b551c30", "sources": ["arxiv", "semantic_scholar"], "title": "Think Locally, Act Globally: Federated Learning with Local and Global Representations", "abstract": "Federated learning is a method of training models on private data distributed over multiple devices. To keep device data private, the global model is trained by only communicating parameters and updates which poses scalability challenges for large models. To this end, we propose a new federated learning algorithm that jointly learns compact local representations on each device and a global model across all devices. As a result, the global model can be smaller since it only operates on local representations, reducing the number of communicated parameters. Theoretically, we provide a generalization analysis which shows that a combination of local and global models reduces both variance in the data as well as variance across device distributions. Empirically, we demonstrate that local models enable communication-efficient training while retaining performance. We also evaluate on the task of personalized mood prediction from real-world mobile data where privacy is key. Finally, local models handle heterogeneous data from new devices, and learn fair representations that obfuscate protected attributes such as race, age, and gender.", "authors": ["Paul Pu Liang", "Terrance Liu", "Liu Ziyin", "Nicholas B. Allen", "Randy P. Auerbach", "David Brent", "Ruslan Salakhutdinov", "Louis-Philippe Morency"], "categories": ["cs.LG", "cs.DC", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-01-06", "url": "https://arxiv.org/abs/2001.01523", "pdf_url": "https://arxiv.org/pdf/2001.01523v3", "arxiv_id": "2001.01523", "doi": null, "citation_count": 737, "influential_citation_count": 90, "has_code": true, "code_url": "https://github.com/pliang279/LG-FedAvg", "venue": "arXiv.org", "quality_score": 0.9795}
{"id": "7c694006e2f19a9e4c3ca944a7f45417bb3da5a646e698217bfb9a8efe91cdca", "sources": ["arxiv", "semantic_scholar"], "title": "Asynchronous Federated Learning with Differential Privacy for Edge Intelligence", "abstract": "Federated learning has been showing as a promising approach in paving the last mile of artificial intelligence, due to its great potential of solving the data isolation problem in large scale machine learning. Particularly, with consideration of the heterogeneity in practical edge computing systems, asynchronous edge-cloud collaboration based federated learning can further improve the learning efficiency by significantly reducing the straggler effect. Despite no raw data sharing, the open architecture and extensive collaborations of asynchronous federated learning (AFL) still give some malicious participants great opportunities to infer other parties' training data, thus leading to serious concerns of privacy. To achieve a rigorous privacy guarantee with high utility, we investigate to secure asynchronous edge-cloud collaborative federated learning with differential privacy, focusing on the impacts of differential privacy on model convergence of AFL. Formally, we give the first analysis on the model convergence of AFL under DP and propose a multi-stage adjustable private algorithm (MAPA) to improve the trade-off between model utility and privacy by dynamically adjusting both the noise scale and the learning rate. Through extensive simulations and real-world experiments with an edge-could testbed, we demonstrate that MAPA significantly improves both the model accuracy and convergence speed with sufficient privacy guarantee.", "authors": ["Yanan Li", "Shusen Yang", "Xuebin Ren", "Cong Zhao"], "categories": ["cs.LG", "math.OC", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-12-17", "url": "https://arxiv.org/abs/1912.07902", "pdf_url": "https://arxiv.org/pdf/1912.07902v1", "arxiv_id": "1912.07902", "doi": null, "citation_count": 43, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4109}
{"id": "013156fead2852f31eab201c6808c172e7e8c0fa6fee0e7e3c858afe02f43c8d", "sources": ["arxiv", "semantic_scholar"], "title": "Private Federated Learning with Domain Adaptation", "abstract": "Federated Learning (FL) is a distributed machine learning (ML) paradigm that enables multiple parties to jointly re-train a shared model without sharing their data with any other parties, offering advantages in both scale and privacy. We propose a framework to augment this collaborative model-building with per-user domain adaptation. We show that this technique improves model accuracy for all users, using both real and synthetic data, and that this improvement is much more pronounced when differential privacy bounds are imposed on the FL model.", "authors": ["Daniel Peterson", "Pallika Kanani", "Virendra J. Marathe"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-12-13", "url": "https://arxiv.org/abs/1912.06733", "pdf_url": "https://arxiv.org/pdf/1912.06733v1", "arxiv_id": "1912.06733", "doi": null, "citation_count": 89, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4886}
{"id": "f3960888242f465fefc9c00cb39bcd35bce8046d1f980286dc46ebccd91b1919", "sources": ["arxiv", "semantic_scholar"], "title": "HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning", "abstract": "Federated learning has emerged as a promising approach for collaborative and privacy-preserving learning. Participants in a federated learning process cooperatively train a model by exchanging model parameters instead of the actual training data, which they might want to keep private. However, parameter interaction and the resulting model still might disclose information about the training data used. To address these privacy concerns, several approaches have been proposed based on differential privacy and secure multiparty computation (SMC), among others. They often result in large communication overhead and slow training time. In this paper, we propose HybridAlpha, an approach for privacy-preserving federated learning employing an SMC protocol based on functional encryption. This protocol is simple, efficient and resilient to participants dropping out. We evaluate our approach regarding the training time and data volume exchanged using a federated learning process to train a CNN on the MNIST data set. Evaluation against existing crypto-based SMC solutions shows that HybridAlpha can reduce the training time by 68% and data transfer volume by 92% on average while providing the same model performance and privacy guarantees as the existing solutions.", "authors": ["Runhua Xu", "Nathalie Baracaldo", "Yi Zhou", "Ali Anwar", "Heiko Ludwig"], "categories": ["cs.CR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2019-12-12", "url": "https://arxiv.org/abs/1912.05897", "pdf_url": "https://arxiv.org/pdf/1912.05897v1", "arxiv_id": "1912.05897", "doi": "10.1145/3338501.3357371", "citation_count": 316, "influential_citation_count": 19, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.6505}
{"id": "4da7656162613c9a81fe3bc6fffb17057b87f568630d2ea355cff0b0c86d0957", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Blockchain Based Federated Learning with Differential Data Sharing", "abstract": "For the modern world where data is becoming one of the most valuable assets, robust data privacy policies rooted in the fundamental infrastructure of networks and applications are becoming an even bigger necessity to secure sensitive user data. In due course with the ever-evolving nature of newer statistical techniques infringing user privacy, machine learning models with algorithms built with respect for user privacy can offer a dynamically adaptive solution to preserve user privacy against the exponentially increasing multidimensional relationships that datasets create. Using these privacy aware ML Models at the core of a Federated Learning Ecosystem can enable the entire network to learn from data in a decentralized manner. By harnessing the ever-increasing computational power of mobile devices, increasing network reliability and IoT devices revolutionizing the smart devices industry, and combining it with a secure and scalable, global learning session backed by a blockchain network with the ability to ensure on-device privacy, we allow any Internet enabled device to participate and contribute data to a global privacy preserving, data sharing network with blockchain technology even allowing the network to reward quality work. This network architecture can also be built on top of existing blockchain networks like Ethereum and Hyperledger, this lets even small startups build enterprise ready decentralized solutions allowing anyone to learn from data across different departments of a company, all the way to thousands of devices participating in a global synchronized learning network.", "authors": ["Anudit Nagar"], "categories": ["cs.CR", "cs.DC", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2019-12-10", "url": "https://arxiv.org/abs/1912.04859", "pdf_url": "https://arxiv.org/pdf/1912.04859v1", "arxiv_id": "1912.04859", "doi": null, "citation_count": 27, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3618}
{"id": "069dd67f1ccd646f8d53b59fb3b7d9a7fcb75dd55b7581a12b7f96fe815ca56e", "sources": ["arxiv", "semantic_scholar"], "title": "Differentially Private Federated Variational Inference", "abstract": "In many real-world applications of machine learning, data are distributed across many clients and cannot leave the devices they are stored on. Furthermore, each client's data, computational resources and communication constraints may be very different. This setting is known as federated learning, in which privacy is a key concern. Differential privacy is commonly used to provide mathematical privacy guarantees. This work, to the best of our knowledge, is the first to consider federated, differentially private, Bayesian learning. We build on Partitioned Variational Inference (PVI) which was recently developed to support approximate Bayesian inference in the federated setting. We modify the client-side optimisation of PVI to provide an ($ε$, $δ$)-DP guarantee. We show that it is possible to learn moderately private logistic regression models in the federated setting that achieve similar performance to models trained non-privately on centralised data.", "authors": ["Mrinank Sharma", "Michael Hutchinson", "Siddharth Swaroop", "Antti Honkela", "Richard E. Turner"], "categories": ["stat.ML", "cs.LG"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-11-24", "url": "https://arxiv.org/abs/1911.10563", "pdf_url": "https://arxiv.org/pdf/1911.10563v1", "arxiv_id": "1911.10563", "doi": null, "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2258}
{"id": "dd39b5de9aec46590cbdb0e3e9c9c7e50b641acd464b2ef88450111f11fb015b", "sources": ["arxiv", "semantic_scholar"], "title": "Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator", "abstract": "Federated Learning is a new distributed learning mechanism which allows model training on a large corpus of decentralized data owned by different data providers, without sharing or leakage of raw data. According to the characteristics of data dis-tribution, it could be usually classified into three categories: horizontal federated learning, vertical federated learning, and federated transfer learning. In this paper we present a solution for parallel dis-tributed logistic regression for vertical federated learning. As compared with existing works, the role of third-party coordinator is removed in our proposed solution. The system is built on the pa-rameter server architecture and aims to speed up the model training via utilizing a cluster of servers in case of large volume of training data. We also evaluate the performance of the parallel distributed model training and the experimental results show the great scalability of the system.", "authors": ["Shengwen Yang", "Bing Ren", "Xuhui Zhou", "Liping Liu"], "categories": ["cs.LG", "cs.CR", "cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2019-11-22", "url": "https://arxiv.org/abs/1911.09824", "pdf_url": "https://arxiv.org/pdf/1911.09824v1", "arxiv_id": "1911.09824", "doi": null, "citation_count": 146, "influential_citation_count": 17, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.6276}
{"id": "a8c9f791ebeb555f7d9e2956bcebc6846f4e77893b4a34c2d830c2beb1e7e726", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning with Bayesian Differential Privacy", "abstract": "We consider the problem of reinforcing federated learning with formal privacy guarantees. We propose to employ Bayesian differential privacy, a relaxation of differential privacy for similarly distributed data, to provide sharper privacy loss bounds. We adapt the Bayesian privacy accounting method to the federated setting and suggest multiple improvements for more efficient privacy budgeting at different levels. Our experiments show significant advantage over the state-of-the-art differential privacy bounds for federated learning on image classification tasks, including a medical application, bringing the privacy budget below 1 at the client level, and below 0.1 at the instance level. Lower amounts of noise also benefit the model accuracy and reduce the number of communication rounds.", "authors": ["Aleksei Triastcyn", "Boi Faltings"], "categories": ["cs.LG", "cs.CR", "cs.DC", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-11-22", "url": "https://arxiv.org/abs/1911.10071", "pdf_url": "https://arxiv.org/pdf/1911.10071v1", "arxiv_id": "1911.10071", "doi": "10.1109/BigData47090.2019.9005465", "citation_count": 211, "influential_citation_count": 9, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.5816}
{"id": "e097d743a5d9f12d6bc419a99bd1048ca4bd12a4a8be5e59ee04f4c5cb344157", "sources": ["arxiv", "semantic_scholar"], "title": "Can You Really Backdoor Federated Learning?", "abstract": "The decentralized nature of federated learning makes detecting and defending against adversarial attacks a challenging task. This paper focuses on backdoor attacks in the federated learning setting, where the goal of the adversary is to reduce the performance of the model on targeted tasks while maintaining good performance on the main task. Unlike existing works, we allow non-malicious clients to have correctly labeled samples from the targeted tasks. We conduct a comprehensive study of backdoor attacks and defenses for the EMNIST dataset, a real-life, user-partitioned, and non-iid dataset. We observe that in the absence of defenses, the performance of the attack largely depends on the fraction of adversaries present and the \"complexity'' of the targeted task. Moreover, we show that norm clipping and \"weak'' differential privacy mitigate the attacks without hurting the overall performance. We have implemented the attacks and defenses in TensorFlow Federated (TFF), a TensorFlow framework for federated learning. In open-sourcing our code, our goal is to encourage researchers to contribute new attacks and defenses and evaluate them on standard federated datasets.", "authors": ["Ziteng Sun", "Peter Kairouz", "Ananda Theertha Suresh", "H. Brendan McMahan"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-11-18", "url": "https://arxiv.org/abs/1911.07963", "pdf_url": "https://arxiv.org/pdf/1911.07963v2", "arxiv_id": "1911.07963", "doi": null, "citation_count": 727, "influential_citation_count": 126, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 1.0}
{"id": "96ec2eb0af34e541a882d826bc375c06342596e5907af835ecb20974dc1f6a11", "sources": ["arxiv", "semantic_scholar"], "title": "Federated and Differentially Private Learning for Electronic Health Records", "abstract": "The use of collaborative and decentralized machine learning techniques such as federated learning have the potential to enable the development and deployment of clinical risk predictions models in low-resource settings without requiring sensitive data be shared or stored in a central repository. This process necessitates communication of model weights or updates between collaborating entities, but it is unclear to what extent patient privacy is compromised as a result. To gain insight into this question, we study the efficacy of centralized versus federated learning in both private and non-private settings. The clinical prediction tasks we consider are the prediction of prolonged length of stay and in-hospital mortality across thirty one hospitals in the eICU Collaborative Research Database. We find that while it is straightforward to apply differentially private stochastic gradient descent to achieve strong privacy bounds when training in a centralized setting, it is considerably more difficult to do so in the federated setting.", "authors": ["Stephen R. Pfohl", "Andrew M. Dai", "Katherine Heller"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Mathematics", "Computer Science"], "published_date": "2019-11-13", "url": "https://arxiv.org/abs/1911.05861", "pdf_url": "https://arxiv.org/pdf/1911.05861v1", "arxiv_id": "1911.05861", "doi": null, "citation_count": 59, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4445}
{"id": "627e4be5728cf21c39dcd0145931e05e1719546612bef1cd3f133c50217317f2", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-Preserving Generalized Linear Models using Distributed Block Coordinate Descent", "abstract": "Combining data from varied sources has considerable potential for knowledge discovery: collaborating data parties can mine data in an expanded feature space, allowing them to explore a larger range of scientific questions. However, data sharing among different parties is highly restricted by legal conditions, ethical concerns, and / or data volume. Fueled by these concerns, the fields of cryptography and distributed learning have made great progress towards privacy-preserving and distributed data mining. However, practical implementations have been hampered by the limited scope or computational complexity of these methods. In this paper, we greatly extend the range of analyses available for vertically partitioned data, i.e., data collected by separate parties with different features on the same subjects. To this end, we present a novel approach for privacy-preserving generalized linear models, a fundamental and powerful framework underlying many prediction and classification procedures. We base our method on a distributed block coordinate descent algorithm to obtain parameter estimates, and we develop an extension to compute accurate standard errors without additional communication cost. We critically evaluate the information transfer for semi-honest collaborators and show that our protocol is secure against data reconstruction. Through both simulated and real-world examples we illustrate the functionality of our proposed algorithm. Without leaking information, our method performs as well on vertically partitioned data as existing methods on combined data -- all within mere minutes of computation time. We conclude that our method is a viable approach for vertically partitioned data analysis with a wide range of real-world applications.", "authors": ["Erik-Jan van Kesteren", "Chang Sun", "Daniel L. Oberski", "Michel Dumontier", "Lianne Ippel"], "categories": ["cs.LG", "cs.CR", "cs.DC", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-11-08", "url": "https://arxiv.org/abs/1911.03183", "pdf_url": "https://arxiv.org/pdf/1911.03183v1", "arxiv_id": "1911.03183", "doi": null, "citation_count": 3, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/vankesteren/privacy-preserving-glm", "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "69862e73b45fce0cd7f61c36cde14c76186e90ca325c0e62a7d20960217ff904", "sources": ["arxiv", "semantic_scholar"], "title": "Enhancing the Privacy of Federated Learning with Sketching", "abstract": "In response to growing concerns about user privacy, federated learning has emerged as a promising tool to train statistical models over networks of devices while keeping data localized. Federated learning methods run training tasks directly on user devices and do not share the raw user data with third parties. However, current methods still share model updates, which may contain private information (e.g., one's weight and height), during the training process. Existing efforts that aim to improve the privacy of federated learning make compromises in one or more of the following key areas: performance (particularly communication cost), accuracy, or privacy. To better optimize these trade-offs, we propose that \\textit{sketching algorithms} have a unique advantage in that they can provide both privacy and performance benefits while maintaining accuracy. We evaluate the feasibility of sketching-based federated learning with a prototype on three representative learning models. Our initial findings show that it is possible to provide strong privacy guarantees for federated learning without sacrificing performance or accuracy. Our work highlights that there exists a fundamental connection between privacy and communication in distributed settings, and suggests important open problems surrounding the theoretical understanding, methodology, and system design of practical, private federated learning.", "authors": ["Zaoxing Liu", "Tian Li", "Virginia Smith", "Vyas Sekar"], "categories": ["cs.LG", "cs.CR", "cs.NI", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-11-05", "url": "https://arxiv.org/abs/1911.01812", "pdf_url": "https://arxiv.org/pdf/1911.01812v1", "arxiv_id": "1911.01812", "doi": null, "citation_count": 22, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3404}
{"id": "04ad3830f7262b8565e2ad9f8cc35ca6355470cade57faa7409a12f039eda403", "sources": ["arxiv", "semantic_scholar"], "title": "Secure and Efficient Federated Transfer Learning", "abstract": "Machine Learning models require a vast amount of data for accurate training. In reality, most data is scattered across different organizations and cannot be easily integrated under many legal and practical constraints. Federated Transfer Learning (FTL) was introduced in [1] to improve statistical models under a data federation that allow knowledge to be shared without compromising user privacy, and enable complementary knowledge to be transferred in the network. As a result, a target-domain party can build more flexible and powerful models by leveraging rich labels from a source-domain party. However, the excessive computational overhead of the security protocol involved in this model rendered it impractical. In this work, we aim towards enhancing the efficiency and security of existing models for practical collaborative training under a data federation by incorporating Secret Sharing (SS). In literature, only the semi-honest model for Federated Transfer Learning has been considered. In this paper, we improve upon the previous solution, and also allow malicious players who can arbitrarily deviate from the protocol in our FTL model. This is much stronger than the semi-honest model where we assume that parties follow the protocol precisely. We do so using the one of the practical MPC protocol called SPDZ, thus our model can be efficiently extended to any number of parties even in the case of a dishonest majority. In addition, the models evaluated in our setting significantly outperform the previous work, in terms of both runtime and communication cost. A single iteration in our model executes in 0.8 seconds for the semi-honest case and 1.4 seconds for the malicious case for 500 samples, as compared to 35 seconds taken by the previous implementation.", "authors": ["Shreya Sharma", "Xing Chaoping", "Yang Liu", "Yan Kang"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2019-10-29", "url": "https://arxiv.org/abs/1910.13271", "pdf_url": "https://arxiv.org/pdf/1910.13271v2", "arxiv_id": "1910.13271", "doi": "10.1109/BigData47090.2019.9006280", "citation_count": 94, "influential_citation_count": 6, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.4944}
{"id": "61703b40ab02be569a5c568e8475a52126f1aa2561bb713ba32b2a567e7d77d3", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data", "abstract": "Recent works have shown that applying Machine Learning to Electronic Health Records (EHR) can strongly accelerate precision medicine. This requires developing models based on diverse EHR sources. Federated Learning (FL) has enabled predictive modeling using distributed training which lifted the need of sharing data and compromising privacy. Since models are distributed in FL, it is attractive to devise ensembles of Deep Neural Networks that also assess model uncertainty. We propose a new FL model called Federated Uncertainty-Aware Learning Algorithm (FUALA) that improves on Federated Averaging (FedAvg) in the context of EHR. FUALA embeds uncertainty information in two ways: It reduces the contribution of models with high uncertainty in the aggregated model. It also introduces model ensembling at prediction time by keeping the last layers of each hospital from the final round. In FUALA, the Federator (central node) sends at each round the average model to all hospitals as well as a randomly assigned hospital model update to estimate its generalization on that hospital own data. Each hospital sends back its model update as well a generalization estimation of the assigned model. At prediction time, the model outputs C predictions for each sample where C is the number of hospital models. The experimental analysis conducted on a cohort of 87K deliveries for the task of preterm-birth prediction showed that the proposed approach outperforms FedAvg when evaluated on out-of-distribution data. We illustrated how uncertainty could be measured using the proposed approach.", "authors": ["Sabri Boughorbel", "Fethi Jarray", "Neethu Venugopal", "Shabir Moosa", "Haithum Elhadi", "Michel Makhlouf"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-10-27", "url": "https://arxiv.org/abs/1910.12191", "pdf_url": "https://arxiv.org/pdf/1910.12191v1", "arxiv_id": "1910.12191", "doi": null, "citation_count": 59, "influential_citation_count": 5, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4445}
{"id": "7a0e541798dfa7fbd2557482d63692839076c63d1d04137dc92ec3c50476fd8f", "sources": ["arxiv", "semantic_scholar"], "title": "Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving", "abstract": "Artificial neural network has achieved unprecedented success in the medical domain. This success depends on the availability of massive and representative datasets. However, data collection is often prevented by privacy concerns and people want to take control over their sensitive information during both training and using processes. To address this problem, we propose a privacy-preserving method for the distributed system, Stochastic Channel-Based Federated Learning (SCBF), which enables the participants to train a high-performance model cooperatively without sharing their inputs. Specifically, we design, implement and evaluate a channel-based update algorithm for the central server in a distributed system, which selects the channels with regard to the most active features in a training loop and uploads them as learned information from local datasets. A pruning process is applied to the algorithm based on the validation set, which serves as a model accelerator. In the experiment, our model presents better performances and higher saturating speed than the Federated Averaging method which reveals all the parameters of local models to the server when updating. We also demonstrate that the saturating rate of performance could be promoted by introducing a pruning process. And further improvement could be achieved by tuning the pruning rate. Our experiment shows that 57% of the time is saved by the pruning process with only a reduction of 0.0047 in AUCROC performance and a reduction of 0.0068 in AUCPR.", "authors": ["Rulin Shao", "Hongyu He", "Hui Liu", "Dianbo Liu"], "categories": ["cs.LG", "cs.CR", "cs.DC", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-10-23", "url": "https://arxiv.org/abs/1910.11160", "pdf_url": "https://arxiv.org/pdf/1910.11160v3", "arxiv_id": "1910.11160", "doi": null, "citation_count": 16, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3076}
{"id": "51eb9130c3dddaa4dad951b1df442733bfb6ba64d5da9e555dfff9b79362242e", "sources": ["arxiv", "semantic_scholar"], "title": "Abnormal Client Behavior Detection in Federated Learning", "abstract": "In federated learning systems, clients are autonomous in that their behaviors are not fully governed by the server. Consequently, a client may intentionally or unintentionally deviate from the prescribed course of federated model training, resulting in abnormal behaviors, such as turning into a malicious attacker or a malfunctioning client. Timely detecting those anomalous clients is therefore critical to minimize their adverse impacts. In this work, we propose to detect anomalous clients at the server side. In particular, we generate low-dimensional surrogates of model weight vectors and use them to perform anomaly detection. We evaluate our solution through experiments on image classification model training over the FEMNIST dataset. Experimental results show that the proposed detection-based approach significantly outperforms the conventional defense-based methods.", "authors": ["Suyi Li", "Yong Cheng", "Yang Liu", "Wei Wang", "Tianjian Chen"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-10-22", "url": "https://arxiv.org/abs/1910.09933", "pdf_url": "https://arxiv.org/pdf/1910.09933v2", "arxiv_id": "1910.09933", "doi": null, "citation_count": 160, "influential_citation_count": 11, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5517}
{"id": "3d6fae9444a25a119422a2c6c510b7a9346b75c2a2e35ca756da9554e16c698c", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Generative Privacy", "abstract": "In this paper, we propose FedGP, a framework for privacy-preserving data release in the federated learning setting. We use generative adversarial networks, generator components of which are trained by FedAvg algorithm, to draw privacy-preserving artificial data samples and empirically assess the risk of information disclosure. Our experiments show that FedGP is able to generate labelled data of high quality to successfully train and validate supervised models. Finally, we demonstrate that our approach significantly reduces vulnerability of such models to model inversion attacks.", "authors": ["Aleksei Triastcyn", "Boi Faltings"], "categories": ["stat.ML", "cs.CR", "cs.DC", "cs.LG"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-10-18", "url": "https://arxiv.org/abs/1910.08385", "pdf_url": "https://arxiv.org/pdf/1910.08385v1", "arxiv_id": "1910.08385", "doi": "10.1109/MIS.2020.2993966", "citation_count": 71, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "IEEE Intelligent Systems", "quality_score": 0.4643}
{"id": "366704ff1db24aa95214b4be3f7c0bbd046c59a4011ddee4369a43f74ea7d5ae", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Learning with Unbiased Gradient Aggregation and Controllable Meta Updating", "abstract": "Federated learning (FL) aims to train machine learning models in the decentralized system consisting of an enormous amount of smart edge devices. Federated averaging (FedAvg), the fundamental algorithm in FL settings, proposes on-device training and model aggregation to avoid the potential heavy communication costs and privacy concerns brought by transmitting raw data. However, through theoretical analysis we argue that 1) the multiple steps of local updating will result in gradient biases and 2) there is an inconsistency between the expected target distribution and the optimization objectives following the training paradigm in FedAvg. To tackle these problems, we first propose an unbiased gradient aggregation algorithm with the keep-trace gradient descent and the gradient evaluation strategy. Then we introduce an additional controllable meta updating procedure with a small set of data samples, indicating the expected target distribution, to provide a clear and consistent optimization objective. Both the two improvements are model- and task-agnostic and can be applied individually or together. Experimental results demonstrate that the proposed methods are faster in convergence and achieve higher accuracy with different network architectures in various FL settings.", "authors": ["Xin Yao", "Tianchi Huang", "Rui-Xiao Zhang", "Ruiyu Li", "Lifeng Sun"], "categories": ["cs.LG", "cs.DC", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-10-18", "url": "https://arxiv.org/abs/1910.08234", "pdf_url": "https://arxiv.org/pdf/1910.08234v3", "arxiv_id": "1910.08234", "doi": null, "citation_count": 79, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4758}
{"id": "f82ae0649acd658cd6221dc585c0560cdd2970110d1c75dc4d28f6dc96fa2260", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-preserving Federated Bayesian Learning of a Generative Model for Imbalanced Classification of Clinical Data", "abstract": "In clinical research, the lack of events of interest often necessitates imbalanced learning. One approach to resolve this obstacle is data integration or sharing, but due to privacy concerns neither is practical. Therefore, there is an increasing demand for a platform on which an analysis can be performed in a federated environment while maintaining privacy. However, it is quite challenging to develop a federated learning algorithm that can address both privacy-preserving and class imbalanced issues. In this study, we introduce a federated generative model learning platform for generating samples in a data-distributed environment while preserving privacy. We specifically propose approximate Bayesian computation-based Gaussian Mixture Model called 'Federated ABC-GMM', which can oversample data in a minor class by estimating the posterior distribution of model parameters across institutions in a privacy-preserving manner. PhysioNet2012, a dataset for prediction of mortality of patients in an Intensive Care Unit (ICU), was used to verify the performance of the proposed method. Experimental results show that our method boosts classification performance in terms of F1 score up to nearly an ideal situation. It is believed that the proposed method can be a novel alternative to solving class imbalance problems.", "authors": ["Seok-Ju Hahn", "Junghye Lee"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-10-18", "url": "https://arxiv.org/abs/1910.08489", "pdf_url": "https://arxiv.org/pdf/1910.08489v3", "arxiv_id": "1910.08489", "doi": null, "citation_count": 5, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "14f88f161ee7d9229594fd3176cb8b4459975fd433c862cb17b66310adb3c91a", "sources": ["arxiv", "semantic_scholar"], "title": "Overcoming Forgetting in Federated Learning on Non-IID Data", "abstract": "We tackle the problem of Federated Learning in the non i.i.d. case, in which local models drift apart, inhibiting learning. Building on an analogy with Lifelong Learning, we adapt a solution for catastrophic forgetting to Federated Learning. We add a penalty term to the loss function, compelling all local models to converge to a shared optimum. We show that this can be done efficiently for communication (adding no further privacy risks), scaling with the number of nodes in the distributed setting. Our experiments show that this method is superior to competing ones for image recognition on the MNIST dataset.", "authors": ["Neta Shoham", "Tomer Avidor", "Aviv Keren", "Nadav Israel", "Daniel Benditkis", "Liron Mor-Yosef", "Itai Zeitak"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-10-17", "url": "https://arxiv.org/abs/1910.07796", "pdf_url": "https://arxiv.org/pdf/1910.07796v1", "arxiv_id": "1910.07796", "doi": null, "citation_count": 279, "influential_citation_count": 23, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.6901}
{"id": "8a799006e7953520fc30c2927aa3241f1b8ab590aa37dacefa225361aa220fbb", "sources": ["arxiv", "semantic_scholar"], "title": "Real-World Image Datasets for Federated Learning", "abstract": "Federated learning is a new machine learning paradigm which allows data parties to build machine learning models collaboratively while keeping their data secure and private. While research efforts on federated learning have been growing tremendously in the past two years, most existing works still depend on pre-existing public datasets and artificial partitions to simulate data federations due to the lack of high-quality labeled data generated from real-world edge applications. Consequently, advances on benchmark and model evaluations for federated learning have been lagging behind. In this paper, we introduce a real-world image dataset. The dataset contains more than 900 images generated from 26 street cameras and 7 object categories annotated with detailed bounding box. The data distribution is non-IID and unbalanced, reflecting the characteristic real-world federated learning scenarios. Based on this dataset, we implemented two mainstream object detection algorithms (YOLO and Faster R-CNN) and provided an extensive benchmark on model performance, efficiency, and communication in a federated learning setting. Both the dataset and algorithms are made publicly available.", "authors": ["Jiahuan Luo", "Xueyang Wu", "Yun Luo", "Anbu Huang", "Yunfeng Huang", "Yang Liu", "Qiang Yang"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-10-14", "url": "https://arxiv.org/abs/1910.11089", "pdf_url": "https://arxiv.org/pdf/1910.11089v3", "arxiv_id": "1910.11089", "doi": null, "citation_count": 111, "influential_citation_count": 5, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5123}
{"id": "13493d0c54fe6970dc46e14f4a4137485d644a1d69f546cdc931c2bbb8aff044", "sources": ["arxiv", "semantic_scholar"], "title": "Central Server Free Federated Learning over Single-sided Trust Social Networks", "abstract": "Federated learning has become increasingly important for modern machine learning, especially for data privacy-sensitive scenarios. Existing federated learning mostly adopts the central server-based architecture or centralized architecture. However, in many social network scenarios, centralized federated learning is not applicable (e.g., a central agent or server connecting all users may not exist, or the communication cost to the central server is not affordable). In this paper, we consider a generic setting: 1) the central server may not exist, and 2) the social network is unidirectional or of single-sided trust (i.e., user A trusts user B but user B may not trust user A). We propose a central server free federated learning algorithm, named Online Push-Sum (OPS) method, to handle this challenging but generic scenario. A rigorous regret analysis is also provided, which shows very interesting results on how users can benefit from communication with trusted users in the federated learning scenario. This work builds upon the fundamental algorithm framework and theoretical guarantees for federated learning in the generic social network scenario.", "authors": ["Chaoyang He", "Conghui Tan", "Hanlin Tang", "Shuang Qiu", "Ji Liu"], "categories": ["cs.LG", "cs.SI", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-10-11", "url": "https://arxiv.org/abs/1910.04956", "pdf_url": "https://arxiv.org/pdf/1910.04956v2", "arxiv_id": "1910.04956", "doi": null, "citation_count": 80, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4771}
{"id": "8f9524e9304bbd22764fd2f13e77b655e59d11ae66078c8374ea2304b931138f", "sources": ["arxiv", "semantic_scholar"], "title": "FedMD: Heterogenous Federated Learning via Model Distillation", "abstract": "Federated learning enables the creation of a powerful centralized model without compromising data privacy of multiple participants. While successful, it does not incorporate the case where each participant independently designs its own model. Due to intellectual property concerns and heterogeneous nature of tasks and data, this is a widespread requirement in applications of federated learning to areas such as health care and AI as a service. In this work, we use transfer learning and knowledge distillation to develop a universal framework that enables federated learning when each agent owns not only their private data, but also uniquely designed models. We test our framework on the MNIST/FEMNIST dataset and the CIFAR10/CIFAR100 dataset and observe fast improvement across all participating models. With 10 distinct participants, the final test accuracy of each model on average receives a 20% gain on top of what's possible without collaboration and is only a few percent lower than the performance each model would have obtained if all private datasets were pooled and made directly available for all participants.", "authors": ["Daliang Li", "Junpu Wang"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-10-08", "url": "https://arxiv.org/abs/1910.03581", "pdf_url": "https://arxiv.org/pdf/1910.03581v1", "arxiv_id": "1910.03581", "doi": null, "citation_count": 1157, "influential_citation_count": 133, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 1.0}
{"id": "ee889be594a20690c566308e32d25a73ef9caf4d3f187ba099144f0c401b893d", "sources": ["arxiv", "semantic_scholar"], "title": "Differential Privacy-enabled Federated Learning for Sensitive Health Data", "abstract": "Leveraging real-world health data for machine learning tasks requires addressing many practical challenges, such as distributed data silos, privacy concerns with creating a centralized database from person-specific sensitive data, resource constraints for transferring and integrating data from multiple sites, and risk of a single point of failure. In this paper, we introduce a federated learning framework that can learn a global model from distributed health data held locally at different sites. The framework offers two levels of privacy protection. First, it does not move or share raw data across sites or with a centralized server during the model training process. Second, it uses a differential privacy mechanism to further protect the model from potential privacy attacks. We perform a comprehensive evaluation of our approach on two healthcare applications, using real-world electronic health data of 1 million patients. We demonstrate the feasibility and effectiveness of the federated learning framework in offering an elevated level of privacy and maintaining utility of the global model.", "authors": ["Olivia Choudhury", "Aris Gkoulalas-Divanis", "Theodoros Salonidis", "Issa Sylla", "Yoonyoung Park", "Grace Hsu", "Amar Das"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2019-10-07", "url": "https://arxiv.org/abs/1910.02578", "pdf_url": "https://arxiv.org/pdf/1910.02578v3", "arxiv_id": "1910.02578", "doi": null, "citation_count": 213, "influential_citation_count": 9, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5826}
{"id": "7e2a48f27cea96f795eff0f606d6caf59e3237dd0b3bd2cbdf3920230d75a014", "sources": ["arxiv", "semantic_scholar"], "title": "Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints", "abstract": "Federated Learning (FL) is currently the most widely adopted framework for collaborative training of (deep) machine learning models under privacy constraints. Albeit it's popularity, it has been observed that Federated Learning yields suboptimal results if the local clients' data distributions diverge. To address this issue, we present Clustered Federated Learning (CFL), a novel Federated Multi-Task Learning (FMTL) framework, which exploits geometric properties of the FL loss surface, to group the client population into clusters with jointly trainable data distributions. In contrast to existing FMTL approaches, CFL does not require any modifications to the FL communication protocol to be made, is applicable to general non-convex objectives (in particular deep neural networks) and comes with strong mathematical guarantees on the clustering quality. CFL is flexible enough to handle client populations that vary over time and can be implemented in a privacy preserving way. As clustering is only performed after Federated Learning has converged to a stationary point, CFL can be viewed as a post-processing method that will always achieve greater or equal performance than conventional FL by allowing clients to arrive at more specialized models. We verify our theoretical analysis in experiments with deep convolutional and recurrent neural networks on commonly used Federated Learning datasets.", "authors": ["Felix Sattler", "Klaus-Robert Müller", "Wojciech Samek"], "categories": ["cs.LG", "cs.DC", "stat.ML"], "fields_of_study": ["Computer Science", "Medicine", "Mathematics"], "published_date": "2019-10-04", "url": "https://arxiv.org/abs/1910.01991", "pdf_url": "https://arxiv.org/pdf/1910.01991v1", "arxiv_id": "1910.01991", "doi": "10.1109/TNNLS.2020.3015958", "citation_count": 1414, "influential_citation_count": 160, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Neural Networks and Learning Systems", "quality_score": 1.0}
{"id": "624f0ec6ae17af78dcb727c2d3f3bc440f8bd7ea8fe27ddcfce3b17b4a012606", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy Preserving Stochastic Channel-Based Federated Learning with Neural Network Pruning", "abstract": "Artificial neural network has achieved unprecedented success in a wide variety of domains such as classifying, predicting and recognizing objects. This success depends on the availability of big data since the training process requires massive and representative data sets. However, data collection is often prevented by privacy concerns and people want to take control over their sensitive information during both training and using processes. To address this problem, we propose a privacy-preserving method for the distributed system, Stochastic Channel-Based Federated Learning (SCBF), which enables the participants to train a high-performance model cooperatively without sharing their inputs. We design, implement and evaluate a channel-based update algorithm for the central server in a distributed system, which selects the channels with regard to the most active features in a training loop and uploads them as learned information from local datasets. A pruning process is applied to the algorithm based on the validation set, which serves as a model accelerator. In the experiment, our model presents equal performances and higher saturating speed than the Federated Averaging method which reveals all the parameters of local models to the server when updating. We also demonstrate that the converging rates could be increased by introducing a pruning process.", "authors": ["Rulin Shao", "Hui Liu", "Dianbo Liu"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2019-10-04", "url": "https://arxiv.org/abs/1910.02115", "pdf_url": "https://arxiv.org/pdf/1910.02115v1", "arxiv_id": "1910.02115", "doi": null, "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2698}
{"id": "93bf301ddcc7046621f996bc17d2daaa90b41b1b242bdb882870e7b67d21bb2b", "sources": ["arxiv", "semantic_scholar"], "title": "Alleviating Privacy Attacks via Causal Learning", "abstract": "Machine learning models, especially deep neural networks have been shown to be susceptible to privacy attacks such as membership inference where an adversary can detect whether a data point was used for training a black-box model. Such privacy risks are exacerbated when a model's predictions are used on an unseen data distribution. To alleviate privacy attacks, we demonstrate the benefit of predictive models that are based on the causal relationships between input features and the outcome. We first show that models learnt using causal structure generalize better to unseen data, especially on data from different distributions than the train distribution. Based on this generalization property, we establish a theoretical link between causality and privacy: compared to associational models, causal models provide stronger differential privacy guarantees and are more robust to membership inference attacks. Experiments on simulated Bayesian networks and the colored-MNIST dataset show that associational models exhibit upto 80% attack accuracy under different test distributions and sample sizes whereas causal models exhibit attack accuracy close to a random guess.", "authors": ["Shruti Tople", "Amit Sharma", "Aditya Nori"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-09-27", "url": "https://arxiv.org/abs/1909.12732", "pdf_url": "https://arxiv.org/pdf/1909.12732v4", "arxiv_id": "1909.12732", "doi": null, "citation_count": 36, "influential_citation_count": 5, "has_code": false, "code_url": null, "venue": "International Conference on Machine Learning", "quality_score": 0.3921}
{"id": "a7a4e88f4480346991f4b98c8b5f82a9f96a53059c27f52cff17d5833b040bb5", "sources": ["arxiv", "semantic_scholar"], "title": "HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography", "abstract": "Electroencephalography (EEG) classification techniques have been widely studied for human behavior and emotion recognition tasks. But it is still a challenging issue since the data may vary from subject to subject, may change over time for the same subject, and maybe heterogeneous. Recent years, increasing privacy-preserving demands poses new challenges to this task. The data heterogeneity, as well as the privacy constraint of the EEG data, is not concerned in previous studies. To fill this gap, in this paper, we propose a heterogeneous federated learning approach to train machine learning models over heterogeneous EEG data, while preserving the data privacy of each party. To verify the effectiveness of our approach, we conduct experiments on a real-world EEG dataset, consisting of heterogeneous data collected from diverse devices. Our approach achieves consistent performance improvement on every task.", "authors": ["Dashan Gao", "Ce Ju", "Xiguang Wei", "Yang Liu", "Tianjian Chen", "Qiang Yang"], "categories": ["eess.SP", "cs.AI"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2019-09-11", "url": "https://arxiv.org/abs/1909.05784", "pdf_url": "https://arxiv.org/pdf/1909.05784v3", "arxiv_id": "1909.05784", "doi": null, "citation_count": 110, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5113}
{"id": "ec2e54a585350fabdb864cabff120c2464d94d008685e82548172639dd5228eb", "sources": ["arxiv", "semantic_scholar"], "title": "Adversary-resilient Distributed and Decentralized Statistical Inference and Machine Learning: An Overview of Recent Advances Under the Byzantine Threat Model", "abstract": "While the last few decades have witnessed a huge body of work devoted to inference and learning in distributed and decentralized setups, much of this work assumes a non-adversarial setting in which individual nodes---apart from occasional statistical failures---operate as intended within the algorithmic framework. In recent years, however, cybersecurity threats from malicious non-state actors and rogue entities have forced practitioners and researchers to rethink the robustness of distributed and decentralized algorithms against adversarial attacks. As a result, we now have a plethora of algorithmic approaches that guarantee robustness of distributed and/or decentralized inference and learning under different adversarial threat models. Driven in part by the world's growing appetite for data-driven decision making, however, securing of distributed/decentralized frameworks for inference and learning against adversarial threats remains a rapidly evolving research area. In this article, we provide an overview of some of the most recent developments in this area under the threat model of Byzantine attacks.", "authors": ["Zhixiong Yang", "Arpita Gang", "Waheed U. Bajwa"], "categories": ["stat.ML", "cs.CR", "cs.DC", "cs.LG", "eess.SP"], "fields_of_study": ["Mathematics", "Computer Science", "Engineering"], "published_date": "2019-08-23", "url": "https://arxiv.org/abs/1908.08649", "pdf_url": "https://arxiv.org/pdf/1908.08649v3", "arxiv_id": "1908.08649", "doi": null, "citation_count": 18, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3197}
{"id": "411e1e9d15d722b758c96ed1ccc41a7d216c16d2e5670617a3579e0712daaee4", "sources": ["arxiv", "semantic_scholar"], "title": "Decentralized Federated Learning: A Segmented Gossip Approach", "abstract": "The emerging concern about data privacy and security has motivated the proposal of federated learning, which allows nodes to only synchronize the locally-trained models instead their own original data. Conventional federated learning architecture, inherited from the parameter server design, relies on highly centralized topologies and the assumption of large nodes-to-server bandwidths. However, in real-world federated learning scenarios the network capacities between nodes are highly uniformly distributed and smaller than that in a datacenter. It is of great challenges for conventional federated learning approaches to efficiently utilize network capacities between nodes. In this paper, we propose a model segment level decentralized federated learning to tackle this problem. In particular, we propose a segmented gossip approach, which not only makes full utilization of node-to-node bandwidth, but also has good training convergence. The experimental results show that even the training time can be highly reduced as compared to centralized federated learning.", "authors": ["Chenghao Hu", "Jingyan Jiang", "Zhi Wang"], "categories": ["cs.LG", "cs.DC", "cs.NI", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-08-21", "url": "https://arxiv.org/abs/1908.07782", "pdf_url": "https://arxiv.org/pdf/1908.07782v1", "arxiv_id": "1908.07782", "doi": null, "citation_count": 220, "influential_citation_count": 15, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.6021}
{"id": "b00c44fee19e658039c4fb34afc3e88a373d4a22d059417d01d215391e0b317e", "sources": ["arxiv", "semantic_scholar"], "title": "Emotionless: Privacy-Preserving Speech Analysis for Voice Assistants", "abstract": "Voice-enabled interactions provide more human-like experiences in many popular IoT systems. Cloud-based speech analysis services extract useful information from voice input using speech recognition techniques. The voice signal is a rich resource that discloses several possible states of a speaker, such as emotional state, confidence and stress levels, physical condition, age, gender, and personal traits. Service providers can build a very accurate profile of a user's demographic category, personal preferences, and may compromise privacy. To address this problem, a privacy-preserving intermediate layer between users and cloud services is proposed to sanitize the voice input. It aims to maintain utility while preserving user privacy. It achieves this by collecting real time speech data and analyzes the signal to ensure privacy protection prior to sharing of this data with services providers. Precisely, the sensitive representations are extracted from the raw signal by using transformation functions and then wrapped it via voice conversion technology. Experimental evaluation based on emotion recognition to assess the efficacy of the proposed method shows that identification of sensitive emotional state of the speaker is reduced by ~96 %.", "authors": ["Ranya Aloufi", "Hamed Haddadi", "David Boyle"], "categories": ["cs.CR", "cs.LG", "cs.SD", "eess.AS", "stat.ML"], "fields_of_study": ["Computer Science", "Engineering", "Mathematics"], "published_date": "2019-08-09", "url": "https://arxiv.org/abs/1908.03632", "pdf_url": "https://arxiv.org/pdf/1908.03632v1", "arxiv_id": "1908.03632", "doi": null, "citation_count": 33, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3829}
{"id": "f963f10550ced4c8b316dd433083e3704f953982d503b9d9385f242083cf371a", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-preserving Distributed Machine Learning via Local Randomization and ADMM Perturbation", "abstract": "With the proliferation of training data, distributed machine learning (DML) is becoming more competent for large-scale learning tasks. However, privacy concerns have to be given priority in DML, since training data may contain sensitive information of users. In this paper, we propose a privacy-preserving ADMM-based DML framework with two novel features: First, we remove the assumption commonly made in the literature that the users trust the server collecting their data. Second, the framework provides heterogeneous privacy for users depending on data's sensitive levels and servers' trust degrees. The challenging issue is to keep the accumulation of privacy losses over ADMM iterations minimal. In the proposed framework, a local randomization approach, which is differentially private, is adopted to provide users with self-controlled privacy guarantee for the most sensitive information. Further, the ADMM algorithm is perturbed through a combined noise-adding method, which simultaneously preserves privacy for users' less sensitive information and strengthens the privacy protection of the most sensitive information. We provide detailed analyses on the performance of the trained model according to its generalization error. Finally, we conduct extensive experiments using real-world datasets to validate the theoretical results and evaluate the classification performance of the proposed framework.", "authors": ["Xin Wang", "Hideaki Ishii", "Linkang Du", "Peng Cheng", "Jiming Chen"], "categories": ["cs.LG", "cs.CR", "cs.DC", "cs.MA", "eess.SY", "stat.ML"], "fields_of_study": ["Computer Science", "Engineering", "Mathematics"], "published_date": "2019-07-30", "url": "https://arxiv.org/abs/1908.01059", "pdf_url": "https://arxiv.org/pdf/1908.01059v2", "arxiv_id": "1908.01059", "doi": "10.1109/TSP.2020.3009007", "citation_count": 48, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Signal Processing", "quality_score": 0.4225}
{"id": "f33d9d32ad12e751fcc088d6f469c4b36f7fde0bda61866b96faa543354ccdb8", "sources": ["arxiv", "semantic_scholar"], "title": "A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection", "abstract": "Federated learning has been a hot research topic in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions. As researchers try to support more machine learning models with different privacy-preserving approaches, there is a requirement in developing systems and infrastructures to ease the development of various federated learning algorithms. Similar to deep learning systems such as PyTorch and TensorFlow that boost the development of deep learning, federated learning systems (FLSs) are equivalently important, and face challenges from various aspects such as effectiveness, efficiency, and privacy. In this survey, we conduct a comprehensive review on federated learning systems. To achieve smooth flow and guide future research, we introduce the definition of federated learning systems and analyze the system components. Moreover, we provide a thorough categorization for federated learning systems according to six different aspects, including data distribution, machine learning model, privacy mechanism, communication architecture, scale of federation and motivation of federation. The categorization can help the design of federated learning systems as shown in our case studies. By systematically summarizing the existing federated learning systems, we present the design factors, case studies, and future research opportunities.", "authors": ["Qinbin Li", "Zeyi Wen", "Zhaomin Wu", "Sixu Hu", "Naibo Wang", "Yuan Li", "Xu Liu", "Bingsheng He"], "categories": ["cs.LG", "cs.CR", "cs.DB", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-07-23", "url": "https://arxiv.org/abs/1907.09693", "pdf_url": "https://arxiv.org/pdf/1907.09693v7", "arxiv_id": "1907.09693", "doi": "10.1109/TKDE.2021.3124599", "citation_count": 1417, "influential_citation_count": 52, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Knowledge and Data Engineering", "quality_score": 0.8621}
{"id": "935cf48ec738dfb20b6a04f56b7f9e85221dc79876d4523f5bb76dadf142d0af", "sources": ["arxiv", "semantic_scholar"], "title": "FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare", "abstract": "With the rapid development of computing technology, wearable devices such as smart phones and wristbands make it easy to get access to people's health information including activities, sleep, sports, etc. Smart healthcare achieves great success by training machine learning models on a large quantity of user data. However, there are two critical challenges. Firstly, user data often exists in the form of isolated islands, making it difficult to perform aggregation without compromising privacy security. Secondly, the models trained on the cloud fail on personalization. In this paper, we propose FedHealth, the first federated transfer learning framework for wearable healthcare to tackle these challenges. FedHealth performs data aggregation through federated learning, and then builds personalized models by transfer learning. It is able to achieve accurate and personalized healthcare without compromising privacy and security. Experiments demonstrate that FedHealth produces higher accuracy (5.3% improvement) for wearable activity recognition when compared to traditional methods. FedHealth is general and extensible and has the potential to be used in many healthcare applications.", "authors": ["Yiqiang Chen", "Jindong Wang", "Chaohui Yu", "Wen Gao", "Xin Qin"], "categories": ["cs.LG", "cs.AI", "cs.NE"], "fields_of_study": ["Computer Science"], "published_date": "2019-07-22", "url": "https://arxiv.org/abs/1907.09173", "pdf_url": "https://arxiv.org/pdf/1907.09173v2", "arxiv_id": "1907.09173", "doi": "10.1109/MIS.2020.2988604", "citation_count": 911, "influential_citation_count": 42, "has_code": false, "code_url": null, "venue": "IEEE Intelligent Systems", "quality_score": 0.8167}
{"id": "d9f9bd080de00032c273b7ef588f77599e99d71c8b5b111697d5c57b9667322c", "sources": ["arxiv", "semantic_scholar"], "title": "Federated Reinforcement Distillation with Proxy Experience Memory", "abstract": "In distributed reinforcement learning, it is common to exchange the experience memory of each agent and thereby collectively train their local models. The experience memory, however, contains all the preceding state observations and their corresponding policies of the host agent, which may violate the privacy of the agent. To avoid this problem, in this work, we propose a privacy-preserving distributed reinforcement learning (RL) framework, termed federated reinforcement distillation (FRD). The key idea is to exchange a proxy experience memory comprising a pre-arranged set of states and time-averaged policies, thereby preserving the privacy of actual experiences. Based on an advantage actor-critic RL architecture, we numerically evaluate the effectiveness of FRD and investigate how the performance of FRD is affected by the proxy memory structure and different memory exchanging rules.", "authors": ["Han Cha", "Jihong Park", "Hyesung Kim", "Seong-Lyun Kim", "Mehdi Bennis"], "categories": ["cs.LG", "cs.MA", "cs.NI", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-07-15", "url": "https://arxiv.org/abs/1907.06536", "pdf_url": "https://arxiv.org/pdf/1907.06536v2", "arxiv_id": "1907.06536", "doi": "10.36227/techrxiv.12645497", "citation_count": 19, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3253}
{"id": "4ee676c53008c0a3c58ece6aedb79f95669b859b7af5af0176092ff80ac3688c", "sources": ["arxiv", "semantic_scholar"], "title": "Multi-hop Federated Private Data Augmentation with Sample Compression", "abstract": "On-device machine learning (ML) has brought about the accessibility to a tremendous amount of data from the users while keeping their local data private instead of storing it in a central entity. However, for privacy guarantee, it is inevitable at each device to compensate for the quality of data or learning performance, especially when it has a non-IID training dataset. In this paper, we propose a data augmentation framework using a generative model: multi-hop federated augmentation with sample compression (MultFAug). A multi-hop protocol speeds up the end-to-end over-the-air transmission of seed samples by enhancing the transport capacity. The relaying devices guarantee stronger privacy preservation as well since the origin of each seed sample is hidden in those participants. For further privatization on the individual sample level, the devices compress their data samples. The devices sparsify their data samples prior to transmissions to reduce the sample size, which impacts the communication payload. This preprocessing also strengthens the privacy of each sample, which corresponds to the input perturbation for preserving sample privacy. The numerical evaluations show that the proposed framework significantly improves privacy guarantee, transmission delay, and local training performance with adjustment to the number of hops and compression rate.", "authors": ["Eunjeong Jeong", "Seungeun Oh", "Jihong Park", "Hyesung Kim", "Mehdi Bennis", "Seong-Lyun Kim"], "categories": ["cs.LG", "cs.NI", "eess.SP", "stat.ML"], "fields_of_study": ["Mathematics", "Computer Science", "Engineering"], "published_date": "2019-07-15", "url": "https://arxiv.org/abs/1907.06426", "pdf_url": "https://arxiv.org/pdf/1907.06426v1", "arxiv_id": "1907.06426", "doi": null, "citation_count": 19, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3253}
{"id": "5d4cc243b47e7f373fc97a19bda8f9d8e120a392725fe5ec17a7fe49e5b90532", "sources": ["arxiv", "semantic_scholar"], "title": "VM Image Repository and Distribution Models for Federated Clouds: State of the Art, Possible Directions and Open Issues", "abstract": "The emerging trend of Federated Cloud models enlist virtualization as a significant concept to offer a large scale distributed Infrastructure as a Service collaborative paradigm to end users. Virtualization leverage Virtual Machines (VM) instantiated from user specific templates labelled as VM Images (VMI). To this extent, the rapid provisioning of VMs with varying user requests ensuring Quality of Service (QoS) across multiple cloud providers largely depends upon the image repository architecture and distribution policies. We discuss the possible state-of-art in VMI storage repository and distribution mechanisms for efficient VM provisioning in federated clouds. In addition, we present and compare various representative systems in this realm. Furthermore, we define a design space, identify current limitations, challenges and open trends for VMI repositories and distribution techniques within federated infrastructure.", "authors": ["Nishant Saurabh", "Dragi Kimovski", "Simon Ostermann", "Radu Prodan"], "categories": ["cs.DC"], "fields_of_study": ["Computer Science"], "published_date": "2019-06-21", "url": "https://arxiv.org/abs/1906.09182", "pdf_url": "https://arxiv.org/pdf/1906.09182v1", "arxiv_id": "1906.09182", "doi": "10.1007/978-3-319-58943-5_21", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Euro-Par 2016: Euro-Par 2016: Parallel Processing Workshops", "quality_score": 0.1505}
{"id": "bf4237601c47efd80e65aea5b32e54206c58bc62ebd5ffd417e5e817c328dc72", "sources": ["arxiv", "semantic_scholar"], "title": "Federated AI lets a team imagine together: Federated Learning of GANs", "abstract": "Envisioning a new imaginative idea together is a popular human need. Imagining together as a team can often lead to breakthrough ideas, but the collaboration effort can also be challenging, especially when the team members are separated by time and space. What if there is a AI that can assist the team to collaboratively envision new ideas?. Is it possible to develop a working model of such an AI? This paper aims to design such an intelligence. This paper proposes a approach to design a creative and collaborative intelligence by employing a form of distributed machine learning approach called Federated Learning along with fusion on Generative Adversarial Networks, GAN. This collaborative creative AI presents a new paradigm in AI, one that lets a team of two or more to come together to imagine and envision ideas that synergies well with interests of all members of the team. In short, this paper explores the design of a novel type of AI paradigm, called Federated AI Imagination, one that lets geographically distributed teams to collaboratively imagine.", "authors": ["Rajagopal. A", "Nirmala. V"], "categories": ["cs.AI", "cs.DC", "cs.MA"], "fields_of_study": ["Computer Science"], "published_date": "2019-06-09", "url": "https://arxiv.org/abs/1906.03595", "pdf_url": "https://arxiv.org/pdf/1906.03595v1", "arxiv_id": "1906.03595", "doi": "10.26438/ijcse/v7i5.704709", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Journal of Computer Science and Engineering", "quality_score": 0.2258}
{"id": "cbb4552cdef57035f8391de08bcf5d00cd4a50d7f98b83ffc041fa652bc72514", "sources": ["arxiv", "semantic_scholar"], "title": "SoK: Differential Privacies", "abstract": "Shortly after it was first introduced in 2006, differential privacy became the flagship data privacy definition. Since then, numerous variants and extensions were proposed to adapt it to different scenarios and attacker models. In this work, we propose a systematic taxonomy of these variants and extensions. We list all data privacy definitions based on differential privacy, and partition them into seven categories, depending on which aspect of the original definition is modified. These categories act like dimensions: variants from the same category cannot be combined, but variants from different categories can be combined to form new definitions. We also establish a partial ordering of relative strength between these notions by summarizing existing results. Furthermore, we list which of these definitions satisfy some desirable properties, like composition, post-processing, and convexity by either providing a novel proof or collecting existing ones.", "authors": ["Damien Desfontaines", "Balázs Pejó"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2019-06-04", "url": "https://arxiv.org/abs/1906.01337", "pdf_url": "https://arxiv.org/pdf/1906.01337v6", "arxiv_id": "1906.01337", "doi": "10.2478/popets-2020-0028", "citation_count": 149, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "Proceedings on Privacy Enhancing Technologies", "quality_score": 0.544}
{"id": "ff800593f5b168ab8491691cc0a3a00025ff32f7de5f8809851ed1f4ccee7588", "sources": ["arxiv", "semantic_scholar"], "title": "Distributed Layer-Partitioned Training for Privacy-Preserved Deep Learning", "abstract": "Deep Learning techniques have achieved remarkable results in many domains. Often, training deep learning models requires large datasets, which may require sensitive information to be uploaded to the cloud to accelerate training. To adequately protect sensitive information, we propose distributed layer-partitioned training with step-wise activation functions for privacy-preserving deep learning. Experimental results attest our method to be simple and effective.", "authors": ["Chun-Hsien Yu", "Chun-Nan Chou", "Emily Chang"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-04-12", "url": "https://arxiv.org/abs/1904.06049", "pdf_url": "https://arxiv.org/pdf/1904.06049v1", "arxiv_id": "1904.06049", "doi": "10.1109/MIPR.2019.00068", "citation_count": 15, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "Conference on Multimedia Information Processing and Retrieval", "quality_score": 0.3495}
{"id": "5533bfa19682d189e16ba10f12249f3dc4fa359b48a06add726b46d2ee65fe69", "sources": ["arxiv", "semantic_scholar"], "title": "Privacy-preserving Active Learning on Sensitive Data for User Intent Classification", "abstract": "Active learning holds promise of significantly reducing data annotation costs while maintaining reasonable model performance. However, it requires sending data to annotators for labeling. This presents a possible privacy leak when the training set includes sensitive user data. In this paper, we describe an approach for carrying out privacy preserving active learning with quantifiable guarantees. We evaluate our approach by showing the tradeoff between privacy, utility and annotation budget on a binary classification task in a active learning setting.", "authors": ["Oluwaseyi Feyisetan", "Thomas Drake", "Borja Balle", "Tom Diethe"], "categories": ["cs.LG", "cs.CL", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2019-03-26", "url": "https://arxiv.org/abs/1903.11112", "pdf_url": "https://arxiv.org/pdf/1903.11112v1", "arxiv_id": "1903.11112", "doi": null, "citation_count": 12, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2785}
{"id": "2a28853e157a7139e2a487d0006fd719f3e513b09b2e5d52936e6c4443987e59", "sources": ["arxiv", "semantic_scholar"], "title": "Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation", "abstract": "Federated learning obtains a central model on the server by aggregating models trained locally on clients. As a result, federated learning does not require clients to upload their data to the server, thereby preserving the data privacy of the clients. One challenge in federated learning is to reduce the client-server communication since the end devices typically have very limited communication bandwidth. This paper presents an enhanced federated learning technique by proposing a synchronous learning strategy on the clients and a temporally weighted aggregation of the local models on the server. In the asynchronous learning strategy, different layers of the deep neural networks are categorized into shallow and deeps layers and the parameters of the deep layers are updated less frequently than those of the shallow layers. Furthermore, a temporally weighted aggregation strategy is introduced on the server to make use of the previously trained local models, thereby enhancing the accuracy and convergence of the central model. The proposed algorithm is empirically on two datasets with different deep neural networks. Our results demonstrate that the proposed asynchronous federated deep learning outperforms the baseline algorithm both in terms of communication cost and model accuracy.", "authors": ["Yang Chen", "Xiaoyan Sun", "Yaochu Jin"], "categories": ["cs.LG", "cs.AI", "cs.DC", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics", "Medicine"], "published_date": "2019-03-18", "url": "https://arxiv.org/abs/1903.07424", "pdf_url": "https://arxiv.org/pdf/1903.07424v1", "arxiv_id": "1903.07424", "doi": "10.1109/TNNLS.2019.2953131", "citation_count": 527, "influential_citation_count": 28, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Neural Networks and Learning Systems", "quality_score": 0.7312}
{"id": "1486139332a2ba72127b71e0a9991bf55f655bd601ecd798f2eaac49eb534f43", "sources": ["arxiv", "semantic_scholar"], "title": "Bayesian Differential Privacy for Machine Learning", "abstract": "Traditional differential privacy is independent of the data distribution. However, this is not well-matched with the modern machine learning context, where models are trained on specific data. As a result, achieving meaningful privacy guarantees in ML often excessively reduces accuracy. We propose Bayesian differential privacy (BDP), which takes into account the data distribution to provide more practical privacy guarantees. We also derive a general privacy accounting method under BDP, building upon the well-known moments accountant. Our experiments demonstrate that in-distribution samples in classic machine learning datasets, such as MNIST and CIFAR-10, enjoy significantly stronger privacy guarantees than postulated by DP, while models maintain high classification accuracy.", "authors": ["Aleksei Triastcyn", "Boi Faltings"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "fields_of_study": ["Mathematics", "Computer Science"], "published_date": "2019-01-28", "url": "https://arxiv.org/abs/1901.09697", "pdf_url": "https://arxiv.org/pdf/1901.09697v5", "arxiv_id": "1901.09697", "doi": null, "citation_count": 2, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}