EasyFL_with_PgFed/applications/fedreid

Federated Person Re-identification (FedReID)

Personal re-identification is an important computer vision task, but its development is constrained by the increasing privacy concerns. Federated learning is a privacy-preserving machine learning technique that learns a shared model across decentralized clients. We implement federated learning to person re-identification (FedReID) and optimize its performance affected by statistical heterogeneity in the real-world scenarios.

This is code for ACMMM 2020 oral paper - Performance Optimization for Federated Person Re-identification via Benchmark Analysis

Algorithm: Federated Partial Averaging (FedPav)

Prerequisite

It requires the following Python libraries:

torch
torchvision
easyfl

Please refer to the documentation to install easyfl.

Datasets

We use 9 popular ReID datasets for the benchmark.

🎉 We are now releasing the processed datasets. (April, 2022)

Please email us to request for the datasets with:

1. A short self-introduction.
2. The purposes of using these datasets.

⚠️ Further distribution of the datasets are prohibited.

Run the experiments

Put the processed datasets in data_dir and run the experiments with the following scripts.

python main.py --data_dir ${data_dir}

You can refer to the main.py to run experiments with more options and configurations.

Note: you can run experiments with multiple GPUs by setting --gpu. The default implementation supports running with multiple GPUs in a slurm cluster. You may need to modify main.py to use multiprocess.

You may refer to the original implementation for the optimization methods: knowledge distillation and weight adjustment.

Citation

@inproceedings{zhuang2020performance,
  title={Performance Optimization of Federated Person Re-identification via Benchmark Analysis},
  author={Zhuang, Weiming and Wen, Yonggang and Zhang, Xuesen and Gan, Xin and Yin, Daiying and Zhou, Dongzhan and Zhang, Shuai and Yi, Shuai},
  booktitle={Proceedings of the 28th ACM International Conference on Multimedia},
  pages={955--963},
  year={2020}
}