Datasets:

fineset-io
/

federated-learning-papers

id string	sources list	title string	abstract string	authors list	categories list	fields_of_study list	published_date timestamp[s]	url string	pdf_url string	arxiv_id string	doi string	citation_count int64	influential_citation_count int64	has_code bool	code_url string	venue string	quality_score float64
eadb479e6e25dd116aaec655854dd5589fe61c44554c151342aa1d36ea98854c	[ "arxiv", "semantic_scholar" ]	Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data	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, ...	[ "Masoume Gholizade", "Fabrizio Ruffini", "Pietro Ducange", "Francesco Marcelloni" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2026-06-09T00:00:00	https://arxiv.org/abs/2606.11272	https://arxiv.org/pdf/2606.11272v1	2606.11272	10.1016/j.neucom.2026.133929	1	0	false	null	Neurocomputing	0.55
4d94643215c1f501a74253686f136f3c7fcb7e9ee53457ac92f513eeebb63f56	[ "arxiv", "semantic_scholar" ]	Cognitive Threat Intelligence and Explainable Federated Security Analytics for distributed Infrastructure Systems	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...	[ "Md. Arifur Rahman", "B. M. Taslimul Haque", "Md. Iqbal Hossan", "Md. Serajul Kabir Chowdhury Rubel" ]	[ "cs.CR", "cs.AI" ]	[ "Computer Science" ]	2026-06-04T00:00:00	https://arxiv.org/abs/2606.05701	https://arxiv.org/pdf/2606.05701v1	2606.05701	10.64882/ijrt.v13.i1.1384	0	0	false	null	International Journal of Research and Technology (IJRT), Volume 13, Issue 01, January-March 2025, pp. 132-151	0.55
76b0d70e6afd3c627a2b97ad5658e2614944870e2b64d193f8ed5a03721ec5d6	[ "arxiv", "semantic_scholar" ]	Federated Learning for Multi-Center Sepsis Early Prediction with Privacy-Preserving	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 ...	[ "Xixi Tian", "Di Wu", "Xiang Liu", "Yiziting Zhu", "Yujie Li", "Xin Shu", "Bin Yi" ]	[ "cs.LG", "cs.CR" ]	[ "Computer Science" ]	2026-06-03T00:00:00	https://arxiv.org/abs/2606.04338	https://arxiv.org/pdf/2606.04338v1	2606.04338	null	0	0	false	null	null	0.35
acbe063f3ed25f024563a10dca445939648e61fb957134c2f212dbc638e7f6e9	[ "arxiv", "semantic_scholar" ]	IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning	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 clie...	[ "Farhin Farhad Riya", "Olivera Kotevska", "Jinyuan Stella Sun" ]	[ "cs.LG", "cs.CR", "cs.DC" ]	[ "Computer Science" ]	2026-06-01T00:00:00	https://arxiv.org/abs/2606.02563	https://arxiv.org/pdf/2606.02563v1	2606.02563	null	0	0	false	null	null	0.35
5a9195622d010f321424727e4a3f35f5f3ea56d523f28eaf156af74f68235a0d	[ "arxiv", "semantic_scholar" ]	GuidaPA: Privacy-Preserving Chatbot for Public Administration via Federated Learning	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 p...	[ "Daniel M. Jimenez-Gutierrez", "Albenzio Cirillo", "Raffaele Nicolussi", "Alessio Beltrame", "Andrea Vitaletti" ]	[ "cs.AI", "cs.CL", "cs.DC", "cs.LG" ]	[ "Computer Science" ]	2026-05-31T00:00:00	https://arxiv.org/abs/2606.01386	https://arxiv.org/pdf/2606.01386v1	2606.01386	null	0	0	false	null	null	0.35
66fcb2d75b922ef1cb67afe5eb0e122e72ea601726caed06b0892380e073fac9	[ "arxiv", "semantic_scholar" ]	Pattern Recognition Tasks with Personalized Federated Learning	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...	[ "Md. Arifur Rahman", "Isha Das", "Mushfiqur Rahman Abir", "B. M. Taslimul Haque", "Abdullah Al Noman", "Abir Ahmed", "Md. Jakir Hossen" ]	[ "cs.CV" ]	[ "Computer Science" ]	2026-05-27T00:00:00	https://arxiv.org/abs/2605.27816	https://arxiv.org/pdf/2605.27816v1	2605.27816	10.28991/ESJ-2026-010-02-020	0	0	false	null	Emerging Science Journal	0.55
64c085ae846117e9ea3c474fb3e7cc32dec30934c132997e8d38b234e6b7b133	[ "arxiv", "semantic_scholar" ]	Federated Learning by Utility-Constrained Stochastic Aggregation for Improving Rational Participation	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 su...	[ "M Yashwanth", "Arunabh Singh", "Ashok Nayak", "Sai Kiran Bulusu", "Anirban Chakraborty" ]	[ "cs.LG" ]	[ "Computer Science" ]	2026-05-18T00:00:00	https://arxiv.org/abs/2605.18020	https://arxiv.org/pdf/2605.18020v1	2605.18020	null	0	0	false	null	null	0.35
922cdbc9ebea3719fbf1cbdc23b9aac5ba2babaf52ad2a382613366131d2acac	[ "arxiv", "semantic_scholar" ]	Privacy-Preserving Generation Fraud Detection for Distributed Photovoltaic Systems: A Solar Irradiance-Fused Federated Learning Framework	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 uncert...	[ "Xiaolu Chen", "Chenghao Huang", "Yanru Zhang", "Hao Wang" ]	[ "cs.LG", "cs.CE" ]	[ "Computer Science" ]	2026-05-16T00:00:00	https://arxiv.org/abs/2605.17039	https://arxiv.org/pdf/2605.17039v1	2605.17039	10.1109/TSG.2026.3692585	0	0	false	null	IEEE Transactions on Smart Grid	0.55
f5a0efd59bdfda557ddc1a95cb5cc60fc05f6e0ed6d60196ec35bea8e1a9dd28	[ "arxiv", "semantic_scholar" ]	Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning	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 t...	[ "Satwat Bashir", "Tasos Dagiuklas", "Muddesar Iqbal" ]	[ "cs.LG" ]	[ "Computer Science" ]	2026-05-12T00:00:00	https://arxiv.org/abs/2605.11815	https://arxiv.org/pdf/2605.11815v1	2605.11815	null	0	0	false	null	null	0.35
e6c402ecb8ca68e36913e884ebe392e1001bb5b9cb5ae343ad3d9bc88d050d45	[ "arxiv", "semantic_scholar" ]	FLAM: Evaluating Model Performance with Aggregatable Measures in Federated Learning	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 me...	[ "Fabian Stricker", "Jose A. Peregrina", "David Bermbach", "Christian Zirpins" ]	[ "cs.LG", "cs.DC" ]	[ "Computer Science" ]	2026-05-08T00:00:00	https://arxiv.org/abs/2605.07962	https://arxiv.org/pdf/2605.07962v1	2605.07962	null	0	0	false	null	null	0.35
19c1ff65552994f383a91d57e6607c6e543bd38cb135c493bf222887e2bcfce2	[ "arxiv", "semantic_scholar" ]	UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment	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 privac...	[ "Shih-Yu Lai", "Hirozumi Yamaguchi", "Shang-Tse Chen", "Yu-Lun Liu", "Bing-Yu Chen" ]	[ "cs.LG", "cs.AI", "cs.CR", "cs.DC" ]	[ "Computer Science" ]	2026-05-08T00:00:00	https://arxiv.org/abs/2605.08288	https://arxiv.org/pdf/2605.08288v1	2605.08288	null	0	0	false	null	null	0.35
0d5ac953659b5d5b8b200ddbe1dd5c92a3b8b52561a5d02f3c420d78d99d7897	[ "arxiv", "semantic_scholar" ]	FL-Sailer: Efficient and Privacy-Preserving Federated Learning for Scalable Single-Cell Epigenetic Data Analysis via Adaptive Sampling	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 dim...	[ "Guangyi Zhang", "Yi Dai", "Yiyun He", "Junhao Liu" ]	[ "cs.LG", "stat.ML" ]	[ "Computer Science", "Mathematics" ]	2026-05-06T00:00:00	https://arxiv.org/abs/2605.04519	https://arxiv.org/pdf/2605.04519v1	2605.04519	null	0	0	false	null	Transactions on Machine Learning Research (TMLR), May 2026	0.55
6fc9bf036962a0236387dd4e9952fd0513753951b8288cd825cda3f0d9c69a28	[ "arxiv", "semantic_scholar" ]	Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning	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 ce...	[ "Judith Sáinz-Pardo Díaz", "Álvaro López García" ]	[ "cs.CR", "cs.AI" ]	[ "Computer Science" ]	2026-05-04T00:00:00	https://arxiv.org/abs/2605.02372	https://arxiv.org/pdf/2605.02372v1	2605.02372	null	0	0	false	null	null	0.35
562b0c01a588450f2dd34c21a7916337bb3f578e4f39c70545183bbafd342519	[ "arxiv", "semantic_scholar" ]	Privacy-Preserving Federated Learning Framework for Distributed Chemical Process Optimization	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...	[ "Teetat Pipattaratonchai", "Aueaphum Aueawatthanaphisut" ]	[ "cs.LG", "cs.AI", "eess.SY" ]	[ "Computer Science", "Engineering" ]	2026-04-28T00:00:00	https://arxiv.org/abs/2604.26073	https://arxiv.org/pdf/2604.26073v1	2604.26073	10.48550/arXiv.2604.26073	0	0	false	null	arXiv.org	0.55
6b7ad21107b2d70f7075053d9c0a0027b77471d6aab2d3ef19f411c67aa27fe0	[ "arxiv", "semantic_scholar" ]	FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels	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 appro...	[ "Sina Gholami", "Abdulmoneam Ali", "Tania Haghighi", "Ahmed Arafa", "Minhaj Nur Alam" ]	[ "cs.LG", "cs.AI", "cs.CV", "cs.DC", "eess.SP" ]	[ "Computer Science", "Engineering" ]	2026-04-22T00:00:00	https://arxiv.org/abs/2604.20825	https://arxiv.org/pdf/2604.20825v1	2604.20825	10.48550/arXiv.2604.20825	0	0	true	https://github.com/sinagh72/FedSIR	arXiv.org	0.85
54814e9e7ffefba53dcf1b8aec2eb1559d4be334bc21033f65b15ed46456965e	[ "arxiv", "semantic_scholar" ]	Secure and Privacy-Preserving Vertical Federated Learning	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...	[ "Shan Jin", "Sai Rahul Rachuri", "Yizhen Wang", "Anderson C. A. Nascimento", "Yiwei Cai" ]	[ "cs.CR", "cs.AI", "cs.DC" ]	[ "Computer Science" ]	2026-04-15T00:00:00	https://arxiv.org/abs/2604.13474	https://arxiv.org/pdf/2604.13474v1	2604.13474	10.48550/arXiv.2604.13474	0	0	false	null	arXiv.org	0.5443
8192a4860026aa41026710ae4865f67246dd1bf932dbb21c2ca518d346d929e0	[ "arxiv", "semantic_scholar" ]	Federated Learning for Privacy-Preserving Medical AI	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 insufficien...	[ "Tin Hoang" ]	[ "cs.LG", "cs.AI", "cs.CV" ]	[ "Computer Science" ]	2026-03-16T00:00:00	https://arxiv.org/abs/2603.15901	https://arxiv.org/pdf/2603.15901v1	2603.15901	10.48550/arXiv.2603.15901	0	0	false	null	arXiv.org	0.5099
40cdc708566be4c1db37bd0f1b85ec1dc7e896f888fb8445483f4a753a51a409	[ "arxiv", "semantic_scholar" ]	Federated Few-Shot Learning on Neuromorphic Hardware: An Empirical Study Across Physical Edge Nodes	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 approximate...	[ "Steven Motta", "Gioele Nanni" ]	[ "cs.NE", "cs.DC", "cs.LG" ]	[ "Computer Science" ]	2026-03-13T00:00:00	https://arxiv.org/abs/2603.13037	https://arxiv.org/pdf/2603.13037v1	2603.13037	10.48550/arXiv.2603.13037	0	0	true	https://github.com/Stemo688/federated-neuromorphic-learning	arXiv.org	0.7827
3d463da2ab49bb6136443e18c4b362c94604c1c87d6780e5747a7127c84fc202	[ "arxiv", "semantic_scholar" ]	Understanding the Resource Cost of Fully Homomorphic Encryption in Quantum Federated Learning	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...	[ "Lukas Böhm", "Arjhun Swaminathan", "Anika Hannemann", "Erik Buchmann" ]	[ "cs.CR" ]	[ "Computer Science" ]	2026-03-03T00:00:00	https://arxiv.org/abs/2603.02799	https://arxiv.org/pdf/2603.02799v1	2603.02799	10.48550/arXiv.2603.02799	0	0	false	null	arXiv.org	0.495
751ba69b2b94bf9a9df91a39068a2f88dd9d057d64739ae6ced6f340400e9af1	[ "arxiv", "semantic_scholar" ]	SRFed: Mitigating Poisoning Attacks in Privacy-Preserving Federated Learning with Heterogeneous Data	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 client...	[ "Yiwen Lu" ]	[ "cs.CR", "cs.DC" ]	[ "Computer Science" ]	2026-02-18T00:00:00	https://arxiv.org/abs/2602.16480	https://arxiv.org/pdf/2602.16480v1	2602.16480	10.48550/arXiv.2602.16480	0	0	false	null	arXiv.org	0.4801
e0e74e8a42015e3f3d1d36ad2d44e87219ce8c498e70698f7dc720e50f41266e	[ "arxiv", "semantic_scholar" ]	Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimization	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 wit...	[ "Farzana Akter", "Rakib Hossain", "Deb Kanna Roy Toushi", "Mahmood Menon Khan", "Sultana Amin", "Lisan Al Amin" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2026-02-17T00:00:00	https://arxiv.org/abs/2602.15304	https://arxiv.org/pdf/2602.15304v1	2602.15304	10.1109/SoutheastCon63549.2026.11476559	1	0	false	null	SoutheastCon	0.479
ffd4c7f8a6af305c960bcd8fb055bb357281c2546865e7a3e06c5fe258f73f4e	[ "arxiv", "semantic_scholar" ]	DeepFusion: Accelerating MoE Training via Federated Knowledge Distillation from Heterogeneous Edge Devices	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...	[ "Songyuan Li", "Jia Hu", "Ahmed M. Abdelmoniem", "Geyong Min", "Haojun Huang", "Jiwei Huang" ]	[ "cs.LG", "cs.AI", "cs.MA" ]	[ "Computer Science" ]	2026-02-15T00:00:00	https://arxiv.org/abs/2602.14301	https://arxiv.org/pdf/2602.14301v1	2602.14301	10.48550/arXiv.2602.14301	0	0	false	null	arXiv.org	0.4767
1a5ccc4a933861100fca2180280d485288ee24081ac0903dba61f0d851332fdd	[ "arxiv", "semantic_scholar" ]	Safeguarding Privacy: Privacy-Preserving Detection of Mind Wandering and Disengagement Using Federated Learning in Online Education	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 i...	[ "Anna Bodonhelyi", "Mengdi Wang", "Efe Bozkir", "Babette Bühler", "Enkelejda Kasneci" ]	[ "cs.LG", "cs.HC" ]	[ "Computer Science" ]	2026-02-10T00:00:00	https://arxiv.org/abs/2602.09904	https://arxiv.org/pdf/2602.09904v1	2602.09904	10.48550/arXiv.2602.09904	1	0	false	null	arXiv.org	0.4709
6beb5eb16c96091446c9a91001bde7a800deadb7276ea79ad321607196278f00	[ "arxiv", "semantic_scholar" ]	Privacy-Preserving Federated Learning with Verifiable Fairness Guarantees	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 cryptographi...	[ "Mohammed Himayath Ali", "Mohammed Aqib Abdullah", "Syed Muneer Hussain", "Mohammed Mudassir Uddin", "Shahnawaz Alam" ]	[ "cs.CR", "cs.CL", "cs.CV" ]	[ "Computer Science" ]	2026-01-18T00:00:00	https://arxiv.org/abs/2601.12447	https://arxiv.org/pdf/2601.12447v2	2601.12447	10.48550/arXiv.2601.12447	0	0	false	null	arXiv.org	0.4446
dd34e512aded99a453059457a24bc96f4ef2059c8c4be307676d17b86dc13330	[ "arxiv", "semantic_scholar" ]	SynQP: A Framework and Metrics for Evaluating the Quality and Privacy Risk of Synthetic Data	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 ...	[ "Bing Hu", "Yixin Li", "Asma Bahamyirou", "Helen Chen" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2026-01-17T00:00:00	https://arxiv.org/abs/2601.12124	https://arxiv.org/pdf/2601.12124v1	2601.12124	10.1109/PST65910.2025.11268831	0	0	true	https://github.com/CAN-SYNH/SynQP	Conference on Privacy, Security and Trust	0.6853
256fa62565650edb8d9a7b2bd7deb00ff8e4b723c902fb875aab34ed4e7c60b3	[ "arxiv", "semantic_scholar" ]	Federated Continual Learning for Privacy-Preserving Hospital Imaging Classification	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 typi...	[ "Anay Sinhal", "Arpana Sinhal", "Amit Sinhal" ]	[ "cs.LG" ]	[ "Computer Science" ]	2026-01-11T00:00:00	https://arxiv.org/abs/2601.06742	https://arxiv.org/pdf/2601.06742v1	2601.06742	10.48550/arXiv.2601.06742	0	0	false	null	arXiv.org	0.4366
249ae56ea8ef0197779b1d67299e0edbafd7df0d9b6bb10f3492984df96d2a96	[ "arxiv", "semantic_scholar" ]	Distributed Federated Learning by Alternating Periods of Training	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 sin...	[ "Shamik Bhattacharyya", "Rachel Kalpana Kalaimani" ]	[ "cs.LG", "eess.SY" ]	[ "Computer Science", "Engineering" ]	2026-01-05T00:00:00	https://arxiv.org/abs/2601.01793	https://arxiv.org/pdf/2601.01793v1	2601.01793	10.48550/arXiv.2601.01793	0	0	false	null	arXiv.org	0.4297
dab73045a79aa8d9f66f8e2f615e930138fb6d186db579ea5b033c8cae8aa884	[ "arxiv", "semantic_scholar" ]	FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs	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 hete...	[ "Zihao Zhou", "Shusen Yang", "Fangyuan Zhao", "Xuebin Ren" ]	[ "cs.LG", "cs.DC" ]	[ "Computer Science" ]	2025-12-29T00:00:00	https://arxiv.org/abs/2512.23235	https://arxiv.org/pdf/2512.23235v1	2512.23235	10.1109/TPDS.2025.3649863	1	0	false	null	IEEE Transactions on Parallel and Distributed Systems	0.4217
369fa8037bef31262bfa174bed0f212651b123b253fd6613df745e14ff20d62f	[ "arxiv", "semantic_scholar" ]	Federated Learning Based Decentralized Adaptive Intelligent Transmission Protocol for Privacy Preserving 6G Networks	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 Lea...	[ "Ansar Ahmed" ]	[ "cs.CR", "cs.AI" ]	[ "Computer Science" ]	2025-12-20T00:00:00	https://arxiv.org/abs/2512.18432	https://arxiv.org/pdf/2512.18432v1	2512.18432	10.48550/arXiv.2512.18432	0	0	false	null	arXiv.org	0.4114
fb45664a0b7373a512087892aebbf7304a81af565c2115df54be4618fd6c7b7f	[ "arxiv", "semantic_scholar" ]	Practical Framework for Privacy-Preserving and Byzantine-robust Federated Learning	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 inf...	[ "Baolei Zhang", "Minghong Fang", "Zhuqing Liu", "Biao Yi", "Peizhao Zhou", "Yuan Wang", "Tong Li", "Zheli Liu" ]	[ "cs.CR", "cs.DC", "cs.LG" ]	[ "Computer Science" ]	2025-12-19T00:00:00	https://arxiv.org/abs/2512.17254	https://arxiv.org/pdf/2512.17254v1	2512.17254	10.1109/TIFS.2025.3642546	2	0	false	null	IEEE Transactions on Information Forensics and Security	0.4102
f21250ac80c887683145b5b6fcd255fe2baf7eb4be0771f6754ed1acb0e74253	[ "arxiv", "semantic_scholar" ]	TrajSyn: Privacy-Preserving Dataset Distillation from Federated Model Trajectories for Server-Side Adversarial Training	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 tra...	[ "Mukur Gupta", "Niharika Gupta", "Saifur Rahman", "Shantanu Pal", "Chandan Karmakar" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-12-17T00:00:00	https://arxiv.org/abs/2512.15123	https://arxiv.org/pdf/2512.15123v1	2512.15123	10.48550/arXiv.2512.15123	0	0	false	null	arXiv.org	0.4079
17a761b9491ecfee2dee24b3f716927b69882058bfa1b836b2b8d820c3e08b05	[ "arxiv", "semantic_scholar" ]	Privacy-Preserving Feature Valuation in Vertical Federated Learning Using Shapley-CMI and PSI Permutation	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 e...	[ "Unai Laskurain", "Aitor Aguirre-Ortuzar", "Urko Zurutuza" ]	[ "cs.CR", "cs.AI", "cs.DC" ]	[ "Computer Science" ]	2025-12-16T00:00:00	https://arxiv.org/abs/2512.14767	https://arxiv.org/pdf/2512.14767v1	2512.14767	10.1109/FLTA67013.2025.11336639	0	0	false	null	null	0.2589
6784621d85097bb650ee4da247055ad4d5569d63b2e19fd9c159fd053613a16b	[ "arxiv", "semantic_scholar" ]	PRIVEE: Privacy-Preserving Vertical Federated Learning Against Feature Inference Attacks	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 probabiliti...	[ "Sindhuja Madabushi", "Ahmad Faraz Khan", "Haider Ali", "Ananthram Swami", "Rui Ning", "Hongyi Wu", "Jin-Hee Cho" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2025-12-14T00:00:00	https://arxiv.org/abs/2512.12840	https://arxiv.org/pdf/2512.12840v1	2512.12840	10.48550/arXiv.2512.12840	0	0	false	null	arXiv.org	0.4045
d1c6360605c27c64f52794a8524df73e9628d9297a0e845226cb11450164d43e	[ "arxiv", "semantic_scholar" ]	Semantic-Constrained Federated Aggregation: Convergence Theory and Privacy-Utility Bounds for Knowledge-Enhanced Distributed Learning	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 Ag...	[ "Jahidul Arafat" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-12-12T00:00:00	https://arxiv.org/abs/2512.15759	https://arxiv.org/pdf/2512.15759v1	2512.15759	10.48550/arXiv.2512.15759	0	0	false	null	arXiv.org	0.4022
54143657b79a6b7ce57b62b017c9dbbd92846371d8c8ea09ef49de0dd108e458	[ "arxiv", "semantic_scholar" ]	A Privacy-Preserving Cloud Architecture for Distributed Machine Learning at Scale	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 complianc...	[ "Vinoth Punniyamoorthy", "Ashok Gadi Parthi", "Mayilsamy Palanigounder", "Ravi Kiran Kodali", "Bikesh Kumar", "Kabilan Kannan" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2025-12-11T00:00:00	https://arxiv.org/abs/2512.10341	https://arxiv.org/pdf/2512.10341v1	2512.10341	10.17577/IJERTV14IS110277	16	1	false	null	arXiv.org	0.401
35a1d4c8e077afb97000dbe24790e7655e4d1f67413c655e8d20f1d887c5e866	[ "arxiv", "semantic_scholar" ]	How to Train Private Clinical Language Models: A Comparative Study of Privacy-Preserving Pipelines for ICD-9 Coding	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 actu...	[ "Mathieu Dufour", "Andrew Duncan" ]	[ "cs.LG", "cs.CL" ]	[ "Computer Science" ]	2025-11-18T00:00:00	https://arxiv.org/abs/2511.14936	https://arxiv.org/pdf/2511.14936v1	2511.14936	10.48550/arXiv.2511.14936	0	0	false	null	arXiv.org	0.3747
a3460bacdf039d45a0bae4fa825ebb9339ab04107c2ad2a71be897397d8e4d32	[ "arxiv", "semantic_scholar" ]	Scaling Law Analysis in Federated Learning: How to Select the Optimal Model Size?	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 ...	[ "Xuanyu Chen", "Nan Yang", "Shuai Wang", "Dong Yuan" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-11-15T00:00:00	https://arxiv.org/abs/2511.12188	https://arxiv.org/pdf/2511.12188v1	2511.12188	10.48550/arXiv.2511.12188	0	0	false	null	AAAI Conference on Artificial Intelligence	0.3713
746a30ebd051766c4c19229a501edc492c59c96b2c23d08f52249d9a940113da	[ "arxiv", "semantic_scholar" ]	Bridging Local and Federated Data Normalization in Federated Learning: A Privacy-Preserving Approach	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 nat...	[ "Melih Coşğun", "Mert Gençtürk", "Sinem Sav" ]	[ "cs.CR" ]	[ "Computer Science" ]	2025-11-14T00:00:00	https://arxiv.org/abs/2511.11249	https://arxiv.org/pdf/2511.11249v1	2511.11249	10.48550/arXiv.2511.11249	0	0	false	null	arXiv.org	0.3701
008508bd50500f1773b2c3652cea6ae756cc9292fda6ae32b9ecbf1ccc15152a	[ "arxiv", "semantic_scholar" ]	Experiences Building Enterprise-Level Privacy-Preserving Federated Learning to Power AI for Science	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 scal...	[ "Zilinghan Li", "Aditya Sinha", "Yijiang Li", "Kyle Chard", "Kibaek Kim", "Ravi Madduri" ]	[ "cs.DC" ]	[ "Computer Science" ]	2025-11-12T00:00:00	https://arxiv.org/abs/2511.08998	https://arxiv.org/pdf/2511.08998v1	2511.08998	10.1109/TPS-ISA67132.2025.00047	2	0	false	null	International Conference on Trust, Privacy and Security in Intelligent Systems and Applications	0.3678
d00643196e53fc4947d7e1f0ac1812ac2d8fe5f0a40dfa491988bdcbacf6d8c0	[ "arxiv", "semantic_scholar" ]	MedHE: Communication-Efficient Privacy-Preserving Federated Learning with Adaptive Gradient Sparsification for Healthcare	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 collaborativ...	[ "Farjana Yesmin" ]	[ "cs.CR", "cs.AI" ]	[ "Computer Science" ]	2025-11-12T00:00:00	https://arxiv.org/abs/2511.09043	https://arxiv.org/pdf/2511.09043v1	2511.09043	10.48550/arXiv.2511.09043	1	0	false	null	arXiv.org	0.3678
ff120f7f2b64ad31798a8dad0ec03cb520f2a3d16bdb4666ae3d4838642eb02c	[ "arxiv", "semantic_scholar" ]	Privacy-Preserving Federated Learning for Fair and Efficient Urban Traffic Optimization	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 pri...	[ "Rathin Chandra Shit", "Sharmila Subudhi" ]	[ "cs.LG", "cs.AI", "cs.NI", "eess.SY" ]	[ "Computer Science", "Engineering" ]	2025-11-09T00:00:00	https://arxiv.org/abs/2511.06363	https://arxiv.org/pdf/2511.06363v1	2511.06363	10.48550/arXiv.2511.06363	0	0	false	null	arXiv.org	0.3644
67f9181eaa6f7f0327c766830d429716933985de635562934ecd65cd0d04729a	[ "arxiv", "semantic_scholar" ]	Federated Cyber Defense: Privacy-Preserving Ransomware Detection Across Distributed Systems	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 organizati...	[ "Daniel M. Jimenez-Gutierrez", "Enrique Zuazua", "Joaquin Del Rio", "Oleksii Sliusarenko", "Xabi Uribe-Etxebarria" ]	[ "cs.CR", "cs.AI", "cs.DC", "cs.LG" ]	[ "Computer Science" ]	2025-11-03T00:00:00	https://arxiv.org/abs/2511.01583	https://arxiv.org/pdf/2511.01583v1	2511.01583	null	0	0	false	null	null	0.2275
26d123f12234e4b8194c6cad3df3ad4940aca0bda53a6754542b763a8bbfa43f	[ "arxiv", "semantic_scholar" ]	Incentive-Based Federated Learning: Architectural Elements and Future Directions	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 u...	[ "Chanuka A. S. Hewa Kaluannakkage", "Rajkumar Buyya" ]	[ "cs.LG", "cs.DC" ]	[ "Computer Science" ]	2025-10-16T00:00:00	https://arxiv.org/abs/2510.14208	https://arxiv.org/pdf/2510.14208v2	2510.14208	10.48550/arXiv.2510.14208	0	0	false	null	arXiv.org	0.3369
0c164c18ce3faf3e70de3f9ab142e3581b6e55ac8a845ebd3f52b80b45b7f5ca	[ "arxiv", "semantic_scholar" ]	Permutation-Invariant Representation Learning for Robust and Privacy-Preserving Feature Selection	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...	[ "Rui Liu", "Tao Zhe", "Yanjie Fu", "Feng Xia", "Ted Senator", "Dongjie Wang" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2025-10-07T00:00:00	https://arxiv.org/abs/2510.05535	https://arxiv.org/pdf/2510.05535v3	2510.05535	10.48550/arXiv.2510.05535	2	0	false	null	arXiv.org	0.3266
2e382b06aaac04ebb1db958140c100a4246f7d86ca1695e5abdbbea70cd88ec8	[ "arxiv", "semantic_scholar" ]	Beyond Static Knowledge Messengers: Towards Adaptive, Fair, and Scalable Federated Learning for Medical AI	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 constr...	[ "Jahidul Arafat", "Fariha Tasmin", "Sanjaya Poudel", "Iftekhar Haider" ]	[ "cs.CY", "cs.LG" ]	[ "Computer Science" ]	2025-10-05T00:00:00	https://arxiv.org/abs/2510.06259	https://arxiv.org/pdf/2510.06259v2	2510.06259	10.48550/arXiv.2510.06259	3	0	false	null	arXiv.org	0.3243
fdfb28817961b30271c450d5c874cfc1a5327f200d39329742a8ab8374dd143c	[ "arxiv", "semantic_scholar" ]	A Lightweight Federated Learning Approach for Privacy-Preserving Botnet Detection in IoT	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 presen...	[ "Taha M. Mahmoud", "Naima Kaabouch" ]	[ "cs.LG", "cs.CR", "cs.DC" ]	[ "Computer Science" ]	2025-10-03T00:00:00	https://arxiv.org/abs/2510.03513	https://arxiv.org/pdf/2510.03513v1	2510.03513	10.1109/ACDSA65407.2025.11165820	0	0	false	null	2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)	0.322
91ecaef0143516788f02c3916143352c1e0e02c9482b4538405dbb74c88dac10	[ "arxiv", "semantic_scholar" ]	Privacy Preserved Federated Learning with Attention-Based Aggregation for Biometric Recognition	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 interpretabili...	[ "Kassahun Azezew", "Minyechil Alehegn", "Tsega Asresa", "Bitew Mekuria", "Tizazu Bayh", "Ayenew Kassie", "Amsalu Tesema", "Animut Embiyale" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-10-01T00:00:00	https://arxiv.org/abs/2510.01113	https://arxiv.org/pdf/2510.01113v1	2510.01113	10.48550/arXiv.2510.01113	1	0	false	null	arXiv.org	0.3197
f89a10f5374d1eec21af848e01ed66b34f4269beeb20ccaf536f4d206056a972	[ "arxiv", "semantic_scholar" ]	OptimES: Optimizing Federated Learning Using Remote Embeddings for Graph Neural Networks	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 ...	[ "Pranjal Naman", "Yogesh Simmhan" ]	[ "cs.DC" ]	[ "Computer Science" ]	2025-09-26T00:00:00	https://arxiv.org/abs/2509.22922	https://arxiv.org/pdf/2509.22922v1	2509.22922	10.48550/arXiv.2506.12425	2	0	false	null	arXiv.org	0.314
af5febb1ba0c3db42969fca9c8cb9ffb947292025c2b7a00ea0af8692c3ff639	[ "arxiv", "semantic_scholar" ]	Distribution-Controlled Client Selection to Improve Federated Learning Strategies	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 caus...	[ "Christoph Düsing", "Philipp Cimiano" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-09-25T00:00:00	https://arxiv.org/abs/2509.20877	https://arxiv.org/pdf/2509.20877v1	2509.20877	10.48550/arXiv.2509.20877	1	0	false	null	arXiv.org	0.3128
b9403695e667aa4370f4026e9e2fa38b56f6460f11a86f48e61df658e0f15888	[ "arxiv", "semantic_scholar" ]	PQFed: A Privacy-Preserving Quality-Controlled Federated Learning Framework	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 individu...	[ "Weiqi Yue", "Wenbiao Li", "Yuzhou Jiang", "Anisa Halimi", "Roger French", "Erman Ayday" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-09-25T00:00:00	https://arxiv.org/abs/2509.21704	https://arxiv.org/pdf/2509.21704v1	2509.21704	10.48550/arXiv.2509.21704	0	0	false	null	arXiv.org	0.3128
2c2d34df8f377d60949ff8238de2ca2bdc353337a56af78bb502e7fedb15f1bc	[ "arxiv", "semantic_scholar" ]	Towards Privacy-Preserving and Heterogeneity-aware Split Federated Learning via Probabilistic Masking	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 attack...	[ "Xingchen Wang", "Feijie Wu", "Chenglin Miao", "Tianchun Li", "Haoyu Hu", "Qiming Cao", "Jing Gao", "Lu Su" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-09-18T00:00:00	https://arxiv.org/abs/2509.14603	https://arxiv.org/pdf/2509.14603v2	2509.14603	10.1145/3770854.3780255	0	0	false	null	Knowledge Discovery and Data Mining	0.3048
6a27b50e0db2a9d1a05cbc4f83c1ba0e8054a7680ccbba80b5f62bce0ea4b08d	[ "arxiv", "semantic_scholar" ]	Privacy Preserving In-Context-Learning Framework for Large Language Models	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 pro...	[ "Bishnu Bhusal", "Manoj Acharya", "Ramneet Kaur", "Colin Samplawski", "Anirban Roy", "Adam D. Cobb", "Rohit Chadha", "Susmit Jha" ]	[ "cs.LG", "cs.CL", "cs.CR" ]	[ "Computer Science" ]	2025-09-17T00:00:00	https://arxiv.org/abs/2509.13625	https://arxiv.org/pdf/2509.13625v4	2509.13625	10.1609/aaai.v40i42.40838	1	0	true	https://github.com/bhusalb/privacy-preserving-icl	AAAI Conference on Artificial Intelligence	0.4693
aa16005083bbdfb3b9fdc4c12023a58a39e592921c9fa1d5d7bd3f51d6975f61	[ "arxiv", "semantic_scholar" ]	Efficient Byzantine-Robust Privacy-Preserving Federated Learning via Dimension Compression	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 priva...	[ "Xian Qin", "Xue Yang", "Xiaohu Tang" ]	[ "cs.CR" ]	[ "Computer Science" ]	2025-09-15T00:00:00	https://arxiv.org/abs/2509.11870	https://arxiv.org/pdf/2509.11870v1	2509.11870	10.1109/TIFS.2026.3671104	1	0	false	null	IEEE Transactions on Information Forensics and Security	0.3014
33721fdbd37020ef5846cac8bdac069e23fd913e50698b80415db2abb83c63eb	[ "arxiv", "semantic_scholar" ]	Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization	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 ...	[ "Kai Yi" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2025-09-10T00:00:00	https://arxiv.org/abs/2509.08233	https://arxiv.org/pdf/2509.08233v1	2509.08233	10.48550/arXiv.2509.08233	0	0	false	null	arXiv.org	0.2956
4199e48aa8d003042ed630a00e27de760174388a216eeebd01bde1a07611306e	[ "arxiv", "semantic_scholar" ]	Accelerating Privacy-Preserving Federated Learning in Large-Scale LEO Satellite Systems	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,...	[ "Binquan Guo", "Junteng Cao", "Marie Siew", "Binbin Chen", "Tony Q. S. Quek", "Zhu Han" ]	[ "cs.LG", "cs.AI", "cs.DC" ]	[ "Computer Science" ]	2025-09-05T00:00:00	https://arxiv.org/abs/2509.12222	https://arxiv.org/pdf/2509.12222v1	2509.12222	10.1109/Trustcom66490.2025.00195	0	0	false	null	International Conference on Trust, Security and Privacy in Computing and Communications	0.2899
e82e4cd9050b08ad6eafc0157efa1f341e635aafbbb866bf692133c03d83edba	[ "arxiv", "semantic_scholar" ]	FedQuad: Federated Stochastic Quadruplet Learning to Mitigate Data Heterogeneity	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 a...	[ "Ozgu Goksu", "Nicolas Pugeault" ]	[ "cs.LG", "cs.CV" ]	[ "Computer Science" ]	2025-09-04T00:00:00	https://arxiv.org/abs/2509.04107	https://arxiv.org/pdf/2509.04107v1	2509.04107	10.1109/FLTA67013.2025.11336470	1	0	false	null	null	0.1837
6285fe94f88d8da77e212ef8aeab62e7004b48c91b1ca0e66fbd6987e11c9cb2	[ "arxiv", "semantic_scholar" ]	AI-in-the-Loop: Privacy Preserving Real-Time Scam Detection and Conversational Scambaiting by Leveraging LLMs and Federated Learning	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...	[ "Ismail Hossain", "Sai Puppala", "Md Jahangir Alam", "Sajedul Talukder" ]	[ "cs.CR", "cs.AI", "cs.LG", "cs.SI" ]	[ "Computer Science" ]	2025-09-04T00:00:00	https://arxiv.org/abs/2509.05362	https://arxiv.org/pdf/2509.05362v4	2509.05362	10.48550/arXiv.2509.05362	3	1	false	null	Proceedings on Privacy Enhancing Technologies	0.2888
925621ad2f75055c0e13214a0c6a4927b140bf9f4df4427650851e72e6d8d93f	[ "arxiv", "semantic_scholar" ]	Gaming and Cooperation in Federated Learning: What Can Happen and How to Monitor It	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-impro...	[ "Dongseok Kim", "Hyoungsun Choi", "Mohamed Jismy Aashik Rasool", "Gisung Oh" ]	[ "cs.LG", "cs.GT", "stat.ML" ]	[ "Computer Science", "Mathematics" ]	2025-09-02T00:00:00	https://arxiv.org/abs/2509.02391	https://arxiv.org/pdf/2509.02391v3	2509.02391	10.48550/arXiv.2509.02391	0	0	false	null	Transactions on Machine Learning Research, 2026	0.2865
1eb9ec1129e27198a4701e3ffc5c0c3f9c13726d4ce9d78241f5ab653e4acf68	[ "arxiv", "semantic_scholar" ]	Federated Distillation on Edge Devices: Efficient Client-Side Filtering for Non-IID Data	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 selec...	[ "Ahmed Mujtaba", "Gleb Radchenko", "Radu Prodan", "Marc Masana" ]	[ "cs.LG", "cs.DC" ]	[ "Computer Science" ]	2025-08-20T00:00:00	https://arxiv.org/abs/2508.14769	https://arxiv.org/pdf/2508.14769v2	2508.14769	10.1109/FLTA67013.2025.11336390	1	0	true	null	null	0.3209
867c052a7cfa9324c42adff43a0a9109360bcce00368bd90a3c6ce14ee2bafaf	[ "arxiv", "semantic_scholar" ]	Beyond Trade-offs: A Unified Framework for Privacy, Robustness, and Communication Efficiency in Federated Learning	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 undermini...	[ "Yue Xia", "Tayyebeh Jahani-Nezhad", "Rawad Bitar" ]	[ "cs.LG", "cs.DC", "cs.IT" ]	[ "Computer Science", "Mathematics" ]	2025-08-18T00:00:00	https://arxiv.org/abs/2508.12978	https://arxiv.org/pdf/2508.12978v2	2508.12978	null	0	0	false	null	null	0.1714
d38e684d5ab8772d898f9e0d392c5a3178b423525c9bc13f806b22573054c0da	[ "arxiv", "semantic_scholar" ]	Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clustering	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 w...	[ "Emmanouil Kritharakis", "Dusan Jakovetic", "Antonios Makris", "Konstantinos Tserpes" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2025-08-18T00:00:00	https://arxiv.org/abs/2508.12672	https://arxiv.org/pdf/2508.12672v4	2508.12672	10.48550/arXiv.2508.12672	3	0	false	null	arXiv.org	0.2693
c6e44aca833dd8d2cd9cf718539ef2f5ffddeef5e6489fb8802dd7a9a8922594	[ "arxiv", "semantic_scholar" ]	Deciphering the Interplay between Attack and Protection Complexity in Privacy-Preserving Federated Learning	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...	[ "Xiaojin Zhang", "Mingcong Xu", "Yiming Li", "Wei Chen", "Qiang Yang" ]	[ "cs.CR", "cs.AI" ]	[ "Computer Science" ]	2025-08-16T00:00:00	https://arxiv.org/abs/2508.11907	https://arxiv.org/pdf/2508.11907v1	2508.11907	10.48550/arXiv.2508.11907	0	0	false	null	arXiv.org	0.267
4f6d307073e76b5509ed4c32199abfc68156d50cf4d3918f3d9b0535d3069996	[ "arxiv", "semantic_scholar" ]	Blockchain-Enabled Federated Learning	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...	[ "Murtaza Rangwala", "KR Venugopal", "Rajkumar Buyya" ]	[ "cs.DC", "cs.LG" ]	[ "Computer Science" ]	2025-08-08T00:00:00	https://arxiv.org/abs/2508.06406	https://arxiv.org/pdf/2508.06406v4	2508.06406	10.48550/arXiv.2508.06406	6	0	false	null	arXiv.org	0.2578
0bf4eae9cc28386bb4d68d4e5f771d1af702a99a35730f3710141d54a2ca641d	[ "arxiv", "semantic_scholar" ]	FedMeNF: Privacy-Preserving Federated Meta-Learning for Neural Fields	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 ...	[ "Junhyeog Yun", "Minui Hong", "Gunhee Kim" ]	[ "cs.LG", "cs.AI", "cs.CV", "cs.DC" ]	[ "Computer Science" ]	2025-08-08T00:00:00	https://arxiv.org/abs/2508.06301	https://arxiv.org/pdf/2508.06301v1	2508.06301	10.1109/ICCV51701.2025.00209	0	0	false	null	IEEE International Conference on Computer Vision	0.2578
4d7ff1dd91c8a65fd17d4e06ed2e10346ab905331ce92c2c337d3444fb7c3ab2	[ "arxiv", "semantic_scholar" ]	From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0	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 sys...	[ "Nuria Rodríguez-Barroso", "Mario García-Márquez", "M. Victoria Luzón", "Francisco Herrera" ]	[ "cs.AI" ]	[ "Computer Science" ]	2025-07-21T00:00:00	https://arxiv.org/abs/2507.15796	https://arxiv.org/pdf/2507.15796v2	2507.15796	10.1016/j.inffus.2026.104236	3	1	false	null	Information Fusion	0.2372
d4d9d82cd9d8b821aebdf6a757133a18835c00f1f102d83a51161a0629822fcb	[ "arxiv", "semantic_scholar" ]	Federated Learning with Graph-Based Aggregation for Traffic Forecasting	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 approa...	[ "Audri Banik", "Glaucio Haroldo Silva de Carvalho", "Renata Dividino" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2025-07-13T00:00:00	https://arxiv.org/abs/2507.09805	https://arxiv.org/pdf/2507.09805v1	2507.09805	10.48550/arXiv.2507.09805	3	0	false	null	arXiv.org	0.228
8640f735b154435ead086f9af4e2234ddfa45e1c08aefde2b2354636cc891f8e	[ "arxiv", "semantic_scholar" ]	FedPhD: Federated Pruning with Hierarchical Learning of Diffusion Models	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 he...	[ "Qianyu Long", "Qiyuan Wang", "Christos Anagnostopoulos", "Daning Bi" ]	[ "cs.LG", "cs.AI", "cs.DC" ]	[ "Computer Science" ]	2025-07-08T00:00:00	https://arxiv.org/abs/2507.06449	https://arxiv.org/pdf/2507.06449v1	2507.06449	10.48550/arXiv.2507.06449	1	0	false	null	arXiv.org	0.2223
5b2b640cbe243336da66d7f9d765fe0f7c682d3c3b411089b1e02d5971aedacc	[ "arxiv", "semantic_scholar" ]	Privacy-Preserving Quantized Federated Learning with Diverse Precision	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 unprotect...	[ "Dang Qua Nguyen", "Morteza Hashemi", "Erik Perrins", "Sergiy A. Vorobyov", "David J. Love", "Taejoon Kim" ]	[ "cs.LG", "eess.SP" ]	[ "Computer Science", "Engineering" ]	2025-07-01T00:00:00	https://arxiv.org/abs/2507.00920	https://arxiv.org/pdf/2507.00920v2	2507.00920	10.48550/arXiv.2507.00920	0	0	false	null	arXiv.org	0.2143
3647235fe3c2688efb7cf794f04813db536da0b89d36060c39c7e9ec8daa0555	[ "arxiv", "semantic_scholar" ]	PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction	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 attac...	[ "Peilin He", "James Joshi" ]	[ "cs.LG", "cs.CR" ]	[ "Computer Science" ]	2025-06-30T00:00:00	https://arxiv.org/abs/2507.00230	https://arxiv.org/pdf/2507.00230v3	2507.00230	10.1109/TPS-ISA67132.2025.00013	0	0	false	null	International Conference on Trust, Privacy and Security in Intelligent Systems and Applications	0.2131
cf405b471f6fc965057f56990afe2b71fd537d39924000856dc591712f7db23c	[ "arxiv", "semantic_scholar" ]	Privacy-Preserving Federated Learning Scheme with Mitigating Model Poisoning Attacks: Vulnerabilities and Countermeasures	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 ...	[ "Jiahui Wu", "Fucai Luo", "Tiecheng Sun", "Haiyan Wang", "Weizhe Zhang" ]	[ "cs.CR" ]	[ "Computer Science" ]	2025-06-30T00:00:00	https://arxiv.org/abs/2506.23622	https://arxiv.org/pdf/2506.23622v2	2506.23622	10.1109/TDSC.2025.3617070	1	0	false	null	IEEE Transactions on Dependable and Secure Computing	0.2131
d627c7ca37ba34f90c9a0f348aacab5831e1e03bf382e25919719546b59be663	[ "arxiv", "semantic_scholar" ]	FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learning	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 d...	[ "Ganyu Wang", "Jinjie Fang", "Maxwell J. Yin", "Bin Gu", "Xi Chen", "Boyu Wang", "Yi Chang", "Charles Ling" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-06-17T00:00:00	https://arxiv.org/abs/2506.14929	https://arxiv.org/pdf/2506.14929v2	2506.14929	10.48550/arXiv.2506.14929	2	0	false	null	International Conference on Machine Learning	0.1982
2dcd9acc56737307694eecd5c39ee21e011b1a7bf008ec74838b0463e0823496	[ "arxiv", "semantic_scholar" ]	Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning	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 clie...	[ "Xiyu Zhao", "Qimei Cui", "Weicai Li", "Wei Ni", "Ekram Hossain", "Quan Z. Sheng", "Xiaofeng Tao", "Ping Zhang" ]	[ "cs.LG", "cs.DC" ]	[ "Computer Science" ]	2025-06-17T00:00:00	https://arxiv.org/abs/2506.14251	https://arxiv.org/pdf/2506.14251v1	2506.14251	10.1109/TMLCN.2025.3528901	1	0	false	null	IEEE Transactions on Machine Learning in Communications and Networking	0.1982
9264a2f270ad9e2aa25b43555eee6a52d8e1c19970dfa4230a26738d8af2627a	[ "arxiv", "semantic_scholar" ]	VFEFL: Privacy-Preserving Federated Learning against Malicious Clients via Verifiable Functional Encryption	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 m...	[ "Nina Cai", "Jinguang Han", "Weizhi Meng" ]	[ "cs.CR", "cs.AI" ]	[ "Computer Science" ]	2025-06-15T00:00:00	https://arxiv.org/abs/2506.12846	https://arxiv.org/pdf/2506.12846v10	2506.12846	10.48550/arXiv.2506.12846	0	0	false	null	Journal of Information Security and Applications	0.1959
54f959aa2df2bf80b082a9881966db6985c6f9f44a7541d547627915efc24526	[ "arxiv", "semantic_scholar" ]	TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems	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 Timber...	[ "Marco Di Gennaro", "Giovanni De Lucia", "Stefano Longari", "Stefano Zanero", "Michele Carminati" ]	[ "cs.CR", "cs.DC", "cs.LG" ]	[ "Computer Science" ]	2025-06-09T00:00:00	https://arxiv.org/abs/2506.07605	https://arxiv.org/pdf/2506.07605v3	2506.07605	10.56553/popets-2025-0145	0	0	false	null	Proceedings on Privacy Enhancing Technologies	0.1891
ee2b08b75c51e46f4b7635e1253d85ca1a18c521036a319853dd7eaeed36be67	[ "arxiv", "semantic_scholar" ]	Federated Learning on Stochastic Neural Networks	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. Fac...	[ "Jingqiao Tang", "Ryan Bausback", "Feng Bao", "Richard Archibald" ]	[ "cs.LG", "cs.DC" ]	[ "Computer Science" ]	2025-06-09T00:00:00	https://arxiv.org/abs/2506.08169	https://arxiv.org/pdf/2506.08169v1	2506.08169	10.48550/arXiv.2506.08169	1	0	false	null	arXiv.org	0.1891
7dab684982c206b0f79f097716936584edc9baca1ea5fe73687ffdf4d35b324e	[ "arxiv", "semantic_scholar" ]	Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review	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. Whil...	[ "Mrinmay Sen", "Shruti Aparna", "Rohit Agarwal", "Chalavadi Krishna Mohan" ]	[ "cs.LG", "cs.DC" ]	[ "Computer Science" ]	2025-06-03T00:00:00	https://arxiv.org/abs/2506.02887	https://arxiv.org/pdf/2506.02887v2	2506.02887	10.48550/arXiv.2506.02887	0	0	false	null	arXiv.org	0.1822
98f0dc552bb621181e5917fe6098c57367a302900e8ef284600dbff7b7c69c3c	[ "arxiv", "semantic_scholar" ]	Blockchain-Enabled Privacy-Preserving Second-Order Federated Edge Learning in Personalized Healthcare	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 we...	[ "Anum Nawaz", "Muhammad Irfan", "Xianjia Yu", "Hamad Aldawsari", "Rayan Hamza Alsisi", "Zhuo Zou", "Tomi Westerlund" ]	[ "cs.LG", "cs.CR", "stat.ML" ]	[ "Computer Science", "Mathematics" ]	2025-05-31T00:00:00	https://arxiv.org/abs/2506.00416	https://arxiv.org/pdf/2506.00416v2	2506.00416	10.1109/TCE.2025.3620115	2	0	false	null	IEEE transactions on consumer electronics	0.1788
b91142b4521d2f8fcdecca5f4cb87da9a2e0d474809ab83e549268b20a23c417	[ "arxiv", "semantic_scholar" ]	Federated Foundation Language Model Post-Training Should Focus on Open-Source Models	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 fo...	[ "Nikita Agrawal", "Ruben Mayer" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-05-29T00:00:00	https://arxiv.org/abs/2505.23593	https://arxiv.org/pdf/2505.23593v4	2505.23593	null	0	0	false	null	null	0.1123
5d3b007b50a090cf3e4bf22b23a18827a0142517a0617bb6f27cb51168f75022	[ "arxiv", "semantic_scholar" ]	CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning	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...	[ "Kaveen Hiniduma", "Zilinghan Li", "Aditya Sinha", "Ravi Madduri", "Suren Byna" ]	[ "cs.CR", "cs.AI", "cs.LG" ]	[ "Computer Science" ]	2025-05-28T00:00:00	https://arxiv.org/abs/2505.23849	https://arxiv.org/pdf/2505.23849v2	2505.23849	10.1109/eScience65000.2025.00023	1	0	false	null	eScience	0.1753
075ecc2a1d651b22084db107e83e73e109d9f7bd3fda709621568a4536a66fa7	[ "arxiv", "semantic_scholar" ]	Federated Deep Reinforcement Learning for Privacy-Preserving Robotic-Assisted Surgery	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 p...	[ "Sana Hafeez", "Sundas Rafat Mulkana", "Muhammad Ali Imran", "Michele Sevegnani" ]	[ "cs.RO" ]	[ "Computer Science" ]	2025-05-17T00:00:00	https://arxiv.org/abs/2505.12153	https://arxiv.org/pdf/2505.12153v2	2505.12153	10.1109/ICDCSW63273.2025.00128	0	0	false	null	International Conference on Distributed Computing Systems Workshops	0.1627
9dd2d704130a7d8989e9f9ba2a319dbe18547ddfcba196eb7e9d643d9101c1fe	[ "arxiv", "semantic_scholar" ]	FedTDP: A Privacy-Preserving and Unified Framework for Trajectory Data Preparation via Federated Learning	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...	[ "Zhihao Zeng", "Ziquan Fang", "Wei Shao", "Lu Chen", "Yunjun Gao" ]	[ "cs.LG", "cs.CR" ]	[ "Computer Science" ]	2025-05-08T00:00:00	https://arxiv.org/abs/2505.05155	https://arxiv.org/pdf/2505.05155v1	2505.05155	10.48550/arXiv.2505.05155	0	0	false	null	arXiv.org	0.1524
0911369fb3f2d36c0720bd15ffe7f0d763d53b26cca544f2095c3c24087c2a51	[ "arxiv", "semantic_scholar" ]	Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning	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 tow...	[ "Md. Tanzib Hosain", "Asif Zaman", "Md. Shahriar Sajid", "Shadman Sakeeb Khan", "Shanjida Akter" ]	[ "cs.LG", "cs.CR", "cs.DC" ]	[ "Computer Science" ]	2025-05-03T00:00:00	https://arxiv.org/abs/2505.01788	https://arxiv.org/pdf/2505.01788v1	2505.01788	10.1109/ICDABI60145.2023.10629638	14	3	false	null	null	0.301
f0d7e31e7b24ab6e6409086fceda8c8b26f3e1577751b8e5cde1f86aaea74b0a	[ "arxiv", "semantic_scholar" ]	Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation	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 fa...	[ "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" ]	[ "cs.CL" ]	[ "Computer Science" ]	2025-04-27T00:00:00	https://arxiv.org/abs/2504.19101	https://arxiv.org/pdf/2504.19101v1	2504.19101	10.48550/arXiv.2504.19101	6	0	false	null	arXiv.org	0.2113
fc9c2e7e2167c5325ad8d8b193898d0e7b835e06a84030bda7aaae93bcb625df	[ "arxiv", "semantic_scholar" ]	Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity	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 a...	[ "Xiaolu Chen", "Chenghao Huang", "Yanru Zhang", "Hao Wang" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2025-04-25T00:00:00	https://arxiv.org/abs/2504.18078	https://arxiv.org/pdf/2504.18078v2	2504.18078	10.1109/TIM.2025.3569908	3	1	false	null	IEEE Transactions on Instrumentation and Measurement	0.1505
b1c5aee83d494dad11aa404b569d3175f83674162f3aee23f4dc5cd15374eade	[ "arxiv", "semantic_scholar" ]	FedDiverse: Tackling Data Heterogeneity in Federated Learning with Diversity-Driven Client Selection	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 o...	[ "Gergely D. Németh", "Eros Fanì", "Yeat Jeng Ng", "Barbara Caputo", "Miguel Ángel Lozano", "Nuria Oliver", "Novi Quadrianto" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2025-04-15T00:00:00	https://arxiv.org/abs/2504.11216	https://arxiv.org/pdf/2504.11216v2	2504.11216	10.1109/FLTA67013.2025.11336421	0	0	false	null	null	0.0802
1fd3f5d5f91d7e271dce8185da3e01481109dbfa812c594ead1f7c08b06c4160	[ "arxiv", "semantic_scholar" ]	PPFPL: Cross-silo Privacy-preserving Federated Prototype Learning Against Data Poisoning Attacks	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 perform...	[ "Hongliang Zhang", "Jiguo Yu", "Fenghua Xu", "Chunqiang Hu", "Yongzhao Zhang", "Xiaofen Wang", "Zhongyuan Yu", "Xiaosong Zhang" ]	[ "cs.CR", "cs.DC" ]	[ "Computer Science" ]	2025-04-04T00:00:00	https://arxiv.org/abs/2504.03173	https://arxiv.org/pdf/2504.03173v5	2504.03173	10.1109/TAI.2025.3643391	1	0	false	null	IEEE Transactions on Artificial Intelligence	0.1134
336a68a2deabc4deeeb4b0584f8fc39190a0eb771a30847dc7687f87bed84bd6	[ "arxiv", "semantic_scholar" ]	Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration	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 e...	[ "Yiwei Zhang", "Jie Liu", "Jiawei Wang", "Lu Dai", "Fan Guo", "Guohui Cai" ]	[ "cs.LG", "cs.CR" ]	[ "Computer Science" ]	2025-03-31T00:00:00	https://arxiv.org/abs/2504.00282	https://arxiv.org/pdf/2504.00282v1	2504.00282	10.1109/ISBDAS64762.2025.11116917	18	0	false	null	null	0.3197
9233072d705b9f26815602f7a782e059ac45fbf91737256dc1fef55e60768267	[ "arxiv", "semantic_scholar" ]	Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning	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 recentl...	[ "Mohammad Khalil", "Ronas Shakya", "Qinyi Liu" ]	[ "cs.LG", "cs.AI", "cs.CR" ]	[ "Computer Science" ]	2025-03-16T00:00:00	https://arxiv.org/abs/2503.13550	https://arxiv.org/pdf/2503.13550v1	2503.13550	10.1109/ICTCS65341.2025.10989403	14	1	false	null	Italian Conference on Theoretical Computer Science	0.294
67f70bb94fa5f80ea6d450ad9da43fec594775e7237f7e52922acc02aefac570	[ "arxiv", "semantic_scholar" ]	Research on Large Language Model Cross-Cloud Privacy Protection and Collaborative Training based on Federated Learning	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 pres...	[ "Ze Yang", "Yihong Jin", "Yihan Zhang", "Juntian Liu", "Xinhe Xu" ]	[ "cs.CR", "cs.AI" ]	[ "Computer Science" ]	2025-03-15T00:00:00	https://arxiv.org/abs/2503.12226	https://arxiv.org/pdf/2503.12226v1	2503.12226	10.1109/AINIT65432.2025.11035133	17	1	false	null	null	0.3138
f1980afe315662812ba745f75368e70155ea29e68495b8ee6da9ce0f3fcfdeb0	[ "arxiv", "semantic_scholar" ]	Ethical AI for Young Digital Citizens: A Call to Action on Privacy Governance	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 vulnerabl...	[ "Austin Shouli", "Ankur Barthwal", "Molly Campbell", "Ajay Kumar Shrestha" ]	[ "cs.CY", "cs.AI", "cs.LG" ]	[ "Computer Science" ]	2025-03-15T00:00:00	https://arxiv.org/abs/2503.11947	https://arxiv.org/pdf/2503.11947v4	2503.11947	10.1002/spy2.70202	6	0	false	null	Security and Privacy	0.2113
70f6aca18b5f96334a63e728993d153eade2593cb8b38e0f132aeb6935e432f5	[ "arxiv", "semantic_scholar" ]	From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges	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 decentra...	[ "Qiongxiu Li", "Wenrui Yu", "Yufei Xia", "Jun Pang" ]	[ "cs.LG", "cs.AI", "cs.DC" ]	[ "Computer Science" ]	2025-03-10T00:00:00	https://arxiv.org/abs/2503.07505	https://arxiv.org/pdf/2503.07505v1	2503.07505	10.48550/arXiv.2503.07505	8	1	false	null	arXiv.org	0.2386
859549dd753d63b73b5a5b798bfe0562a21a2e9b81f54f431907168d25782e88	[ "arxiv", "semantic_scholar" ]	Right Reward Right Time for Federated Learning	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 homoge...	[ "Thanh Linh Nguyen", "Dinh Thai Hoang", "Diep N. Nguyen", "Quoc-Viet Pham" ]	[ "cs.LG", "cs.AI", "cs.DC", "cs.GT" ]	[ "Computer Science" ]	2025-03-10T00:00:00	https://arxiv.org/abs/2503.07869	https://arxiv.org/pdf/2503.07869v3	2503.07869	10.48550/arXiv.2503.07869	2	0	false	null	arXiv.org	0.1193

End of preview. Expand in Data Studio

Federated Learning Papers — FineSet

A research-paper dataset on Federated Learning Papers, assembled, deduplicated, and quality-scored by FineSet from arXiv and Semantic Scholar.

📸 This is a dated snapshot — generated 2026-06-19. It is not auto-updated. Research on Federated Learning Papers moves fast — new papers land on arXiv every week. Want this same dataset refreshed daily, on a topic you choose? See the bottom. ↓

Why this dataset

Quality-scored: quality_score float (0–1), blends citations with recency + code/venue signals — filter out the noise
Papers with code: 49 flagged via has_code — find reproducible work fast
Deduplicated: arXiv + Semantic Scholar cross-referenced, duplicate records merged
Clean JSONL: 481 records, one per line, normalized fields — no encoding garbage

Dataset details

Records: 481
Date range: 2019–2026
Snapshot date: 2026-06-19 (frozen — see note above)
Sources: arXiv, Semantic Scholar (cross-referenced, duplicates merged)
arXiv categories: cs.LG, cs.CR
Quality scoring: citations + recency + code/venue blend, 0–1 (p50=0.314, p90=0.53)
Format: JSONL, one record per line

Fields

Field	Type	Description
id	string	Deterministic SHA256 record id
sources	list	Which sources contributed (`arxiv`, `semantic_scholar`)
title	string	Paper title
abstract	string	Full abstract
authors	list	Author names
categories	list	arXiv category codes
fields_of_study	list	Semantic Scholar field tags
published_date	string	ISO 8601 date
url	string	arXiv abstract URL
pdf_url	string\|null	Open-access PDF if available
arxiv_id	string\|null	arXiv identifier
doi	string\|null	DOI if available
citation_count	int	Citation count (Semantic Scholar)
influential_citation_count	int	Influential citations (Semantic Scholar)
has_code	bool	Code repo detected in the arXiv comment
code_url	string\|null	GitHub URL if detected
venue	string\|null	Publication venue
quality_score	float	0–1, blended (citations + recency + code/venue)

Quality score methodology

quality_score = max(impact, freshness), clamped to [0, 1], where:

impact = max( log10(citations+1)/4 , log10(influential_citations+1)/2 ) — realized impact (0.5 at 100 citations, ~0.75 at 1,000, 1.0 at 10,000+).
freshness = recency × (0.35 + 0.30·has_code + 0.20·has_venue) — a baseline for recent papers (so a strong paper published this week isn't scored 0 just for lacking citations), where recency is 1.0 for papers ≤60 days old and decays linearly to 0 by ~18 months.

Old highly-cited papers score on impact; brand-new papers score on freshness; old uncited papers score ~0. Useful for filtering training data by quality, not just age.

👉 Want this on YOUR topic, updated daily?

This snapshot is frozen at 2026-06-19. The live FineSet pipeline keeps a dataset like this refreshed every day on whatever topic you describe — new papers in, dedup and quality scoring automatic, export as JSONL/Parquet or push straight to the Hub.

Tell me the topic you'd want and I'll run the pipeline on it — open a discussion on this dataset, it's free and it's how I decide what to build next.

→ fineset.io — describe what you want to train on, get a dataset. Early-access waitlist open (referral skip available).

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