Upload 3 files

CITATION.cff

@@ -0,0 +1,62 @@
+cff-version: 1.2.0
+type: dataset
+title: "FLKD 3D Benchmark: Benchmarking Federated Learning and Knowledge Distillation for Point Cloud Classification"
+authors:
+  - family-names: "Aiersilan"
+    given-names: "Aizierjiang"
+version: 1.0.0
+date-released: 2026-06-29
+description: "Pre-trained models and experimental results from the comprehensive benchmark evaluating 13 Federated Learning algorithms, 11 Knowledge Distillation objectives, and their combinations on 3D point cloud classification tasks, specifically on the Craniosynostosis medical dataset. This collection includes model checkpoints, training logs, metrics, and configurations for reproducible research in federated learning and knowledge distillation for 3D deep learning."
+keywords:
+  - federated-learning
+  - knowledge-distillation
+  - point-cloud
+  - 3d-classification
+  - benchmarking
+  - deep-learning
+  - privacy-preserving-ml
+  - distributed-training
+license: MIT
+repository-code: "https://github.com/Ezharjan/FLKD3DBenchmark"
+url: "https://ezharjan.github.io/FLKD3DBenchmark"
+references:
+  - type: conference-paper
+    authors:
+      - family-names: "Aiersilan"
+        given-names: "Aizierjiang"
+    title: "Benchmarking Federated Learning and Knowledge Distillation for Point Cloud Classification"
+    conference:
+      name: "European Conference on Computer Vision"
+      acronym: "ECCV"
+    year: 2026
+    publisher:
+      name: "Springer"
+    doi: "10.1007/XXX-X-XXX-XXXXX-X"
+contact:
+  - type: person
+    name: "Aizierjiang Aiersilan"
+    email: "alx.laboratory@gmail.com"
+
+# Usage & Citation Information
+# ==============================
+#
+# If you use this dataset or models in your research, please cite the original paper:
+#
+# BibTeX format:
+# @inproceedings{aizierjiang26benchmark,
+#   title={Benchmarking Federated Learning and Knowledge Distillation for Point Cloud Classification},
+#   author={Aizierjiang Aiersilan},
+#   booktitle={European Conference on Computer Vision},
+#   organization={Springer},
+#   year={2026}
+# }
+#
+# Chicago style:
+# Aiersilan, Aizierjiang. "Benchmarking Federated Learning and Knowledge Distillation
+# for Point Cloud Classification." Presented at the European Conference on Computer Vision, 2026.
+#
+# APA style:
+# Aiersilan, A. (2026). Benchmarking federated learning and knowledge distillation for
+# point cloud classification. In European Conference on Computer Vision. Springer.
+#
+# We appreciate your citation! It helps us track usage and improve future work.

MODEL_CARD.md

@@ -0,0 +1,83 @@
+# Model Card: FLKD Point Cloud Classification Benchmark
+
+## Model Details
+
+### Model Family
+PointNet++ (Single-Scale Grouping) with optional Federated Learning + Knowledge Distillation training
+
+### Model Variants
+- **Classical**: Standard centralized training
+- **Federated**: Trained via 13 different federated learning algorithms (FedAvg, FedProx, SCAFFOLD, FedDyn, FedAvgM, FedAdam, FedYogi, FedAdagrad, FedMedian, FedBN, MOON, Ditto, FedNova)
+
+### Input/Output
+
+**Input**: 
+- 3D point clouds with variable number of points (typically 1024 points)
+- Float32 tensors of shape (batch_size, num_points, 3)
+
+**Output**: 
+- Class logits for binary classification (2 classes)
+- Softmax probabilities for disease classification
+
+## Intended Use
+
+These models are designed for **3D point cloud classification**. They serve as benchmarks for evaluating:
+
+1. **Federated Learning** effectiveness on 3D data with heterogeneous client distributions
+2. **Knowledge Distillation** techniques applied to point cloud models
+3. **Combined FL+KD** approaches for privacy-preserving distributed training
+4. **Model performance variance** across multiple random seeds
+
+### Primary Use Cases
+- Research on federated learning for 3D deep learning
+- Comparison of knowledge distillation losses on distributed systems
+- Benchmark reference for new FL/KD algorithms on 3D data
+
+
+## Model Performance
+
+
+## Hardware & Training
+
+### Training Environment
+- **GPU**: NVIDIA GPUs; compatible with newer CUDA architectures
+- **Distributed**: Single-GPU per job; parallel jobs via SLURM job arrays
+- **Framework**: PyTorch 2.0+
+## Recommendations
+
+### How to Use
+1. Load `best_model.pth` for production inference
+2. Preprocess input point clouds to match training data distribution
+3. Use model in evaluation mode (`model.eval()`) for inference
+4. Report metrics across multiple seeds for reproducibility
+
+### How to Cite
+When using these models, please cite the original paper and dataset:
+
+```bibtex
+@inproceedings{aizierjiang26benchmark,
+  title={Benchmarking Federated Learning and Knowledge Distillation for Point Cloud Classification},
+  author={Aizierjiang Aiersilan},
+  booktitle={European Conference on Computer Vision},
+  organization={Springer},
+  year={2026}
+}
+```
+
+## Versioning
+
+- **Model Version**: 1.0 (ECCV 2026 publication)
+- **Framework**: PyTorch 2.0+
+- **Python Version**: 3.10+
+
+## Contact & Support
+
+For questions about these models, please visit:
+- **Project Website**: https://ezharjan.github.io/FLKD3DBenchmark/
+- **GitHub Repository**: https://github.com/Ezharjan/FLKD3DBenchmark/
+
+---
+
+**Model Card Last Updated**: June 2026  
+**Model Type**: Supervised Classification (3D Point Clouds)  
+**Task**: Federated Learning + Knowledge Distillation Benchmark

README.md

@@ -1,3 +1,157 @@
+# FLKD 3D Benchmark - Output Models and Results
+
+This directory contains pre-trained models and comprehensive results from the **Benchmarking Federated Learning and Knowledge Distillation for Point Cloud Classification** benchmark (ECCV 2026).
+
+## Overview
+
+This collection includes trained PointNet++ models evaluated on the 3D point cloud datasets across two training paradigms:
+
+- **Classical Training**: Centralized, single-machine model training
+- **Federated Learning + Knowledge Distillation (FLKD)**: Distributed federated learning with knowledge distillation objectives
+
+All experiments were conducted with multiple random seeds to ensure robust statistical reporting of performance metrics.
+
+## Directory Structure
+
+```
+RootDir/
+└── <dataset_name></dataset_name>/
+    ├── classification/
+    │   └── pointnet2_cls_ssg_s{seed}/
+    │       ├── checkpoints/
+    │       │   ├── best_model.pth
+    │       │   ├── last_model.pth
+    │       │   └── resume.pth
+    │       ├── config.json
+    │       ├── logs/
+    │       │   └── train.log
+    │       └── metrics.jsonl
+    │
+    └── flkd/federated/classification/
+        └── fl_pointnet2_cls_ssg_{algorithm}_s{seed}/
+            ├── checkpoints/
+            │   ├── best_model.pth
+            │   ├── last_model.pth
+            │   └── resume.pth
+            ├── config.json
+            ├── logs/
+            │   └── train.log
+            └── metrics.jsonl
+```
+
+## File Descriptions
+
+### Checkpoints
+- **best_model.pth**: Model weights achieving the highest validation accuracy during training
+- **last_model.pth**: Model weights from the final training round/epoch
+- **resume.pth**: Complete training state (model, optimizer, RNG) for resuming interrupted training
+
+### Configuration
+- **config.json**: Hyperparameters, dataset splits, model architecture, and training settings used for this experiment
+
+### Logs
+- **train.log**: Detailed per-epoch/round training logs including loss values and validation metrics
+
+### Metrics
+- **metrics.jsonl**: Machine-readable results in JSONL format containing:
+  - Per-epoch/round accuracy metrics
+  - Loss values
+  - Training time information
+  - Other performance indicators
+
+## Seeds and Reproducibility
+
+Experiments use multiple random seeds (e.g., s7, s42, s123) to report mean ± standard deviation statistics, ensuring robust statistical conclusions. Load `best_model.pth` for production use; consult `metrics.jsonl` for full seed-wise performance breakdowns.
+
+## Federated Learning Algorithms
+
+The FLKD benchmark evaluates these 13 FL algorithms:
+- **FedAvg**: Classical federated averaging (also called "vanilla")
+- **FedProx**: Proximal term regularization for heterogeneous local objectives
+- **SCAFFOLD**: Control variates to reduce client drift
+- **FedDyn**: Biased aggregation with consensus optimization
+- **FedAvgM**: Momentum-based federated averaging
+- **FedAdam**: Server-side adaptive learning rates (Adam variant)
+- **FedYogi**: Server-side adaptive learning rates (Yogi variant)
+- **FedAdagrad**: Server-side adaptive learning rates (AdaGrad variant)
+- **FedMedian**: Robust aggregation via median
+- **FedBN**: Batch norm personalization for heterogeneous local data
+- **MOON**: Contrastive learning to maintain consistency
+- **Ditto**: Explicit client-local personalization
+- **FedNova**: Normalized aggregation for non-IID data
+
+
+## Model Architecture
+
+**PointNet++ (Single-Scale Grouping / SSG)**:
+- Multi-layer hierarchical feature learning on point clouds
+- Set Abstraction (SA) layers with ball query and PointNet modules
+- Feature Propagation (FP) layers for upsampling
+- Designed for robust 3D shape understanding
+
+## Usage
+
+### Loading a Model
+
+```python
+import torch
+
+# Load the best model for a specific configuration
+model = torch.load('pointnet2_cls_ssg_s123/checkpoints/best_model.pth')
+
+# Or load with full training state (for resuming)
+checkpoint = torch.load('pointnet2_cls_ssg_s123/checkpoints/resume.pth')
+model_state = checkpoint['model_state']
+optimizer_state = checkpoint['optimizer_state']
+```
+
+### Accessing Results
+
+```python
+import json
+
+# Load configuration
+with open('pointnet2_cls_ssg_s123/config.json') as f:
+    config = json.load(f)
+
+# Read metrics (each line is a JSON object)
+with open('pointnet2_cls_ssg_s123/metrics.jsonl') as f:
+    for line in f:
+        epoch_metrics = json.loads(line)
+        print(epoch_metrics)
+```
+
+## Citation
+
+If you use these models or results, please cite the original paper:
+
+```bibtex
+@inproceedings{aizierjiang26benchmark,
+  title={Benchmarking Federated Learning and Knowledge Distillation for Point Cloud Classification},
+  author={Aizierjiang Aiersilan},
+  booktitle={European Conference on Computer Vision},
+  organization={Springer},
+  year={2026}
+}
+```
+
+## License
+
+The models and code follow the project's original license. Please refer to the main repository for detailed license information.
+
+## Additional Resources
+
+- **Project Website**: https://ezharjan.github.io/FLKD3DBenchmark/
+- **Main Repository**: https://github.com/Ezharjan/FLKD3DBenchmark/
+- **Paper**: Available at the project website
+
+## Notes
+
+- All models were trained using PyTorch with CUDA acceleration
+- Mixed precision (bf16) was applied where supported; exact fp32 on high performance GPUs
+- Auto-resume checkpoints enable resuming interrupted training without loss of progress
+- Metrics are reported as mean ± std across multiple random seeds for robust statistical assessment
+
 ---
-license: mit
----
+
+For questions, issues, or to contribute improvements, please visit the main repository or contact the authors through the project website.