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ezharjan/
FLKD3DBenchmark

Name: FLKD3DBenchmark
Availability: InStock

federated-learning

knowledge-distillation

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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

For questions, issues, or to contribute improvements, please visit the main repository or contact the authors through the project website.

ezharjan/FLKD3DBenchmark