MODEL_CARD.md

metadata

license: mit
tags:
  - federated-learning
  - knowledge-distillation
  - point-cloud
  - 3d-classification
  - benchmarking
  - privacy-preserving
  - distributed-training
datasets:
  - craniosynostosis
  - modelnet40
metrics:
  - accuracy
  - macro-accuracy
language:
  - en
model-index:
  - name: FLKD Benchmark Models
    results:
      - task:
          name: 3D Point Cloud Classification
          type: image-classification
        metrics:
          - name: Accuracy
            type: accuracy
          - name: Macro Accuracy
            type: accuracy

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:

@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/

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Model Card Last Updated: June 2026
Model Type: Supervised Classification (3D Point Clouds)
Task: Federated Learning + Knowledge Distillation Benchmark