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Code for ACMMM 2020 oral paper -Performance Optimization for Federated Person Re-identification via Benchmark Analysis

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. In this work, we implement federated learning to person re-identification (FedReID) and optimize its performance affected bystatistical heterogeneity in the real-world scenario.

Algorithm: Federated Partial Averaging (FedPav)

• Install the libraries listed in requirements.txt

  pip install -r requirements.txt

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

Pleaseemail us (ccthis email in case it is filtered as spam) 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.

We use 9 popular ReID datasets for the benchmark.

You can obtain the datasets fromawesome-reid-dataset

Dataset folder structure after preprocessing is providedhere

You can follow the following steps to preprocess datasets:

1. Download all datasets todata_preprocess/data folder.
2. We provide the Json files for spliting the small datasets. (We haven't officially release thesplit.json files. Please send an email with short introduction to request for them.)
3. Run the following script to prepare all datasets:

   python prepare_all_datasets.py

4. Move thedata folder to the root directory.

   move data_preprocess/data ./

5. For federated-by-identity scenario:

   python split_id_data.py

6. For federated-by-camera scenario:

   python split_camera_data.py

7. For merging all datasets to do merge training, you can userename_dataset.py andmix_datasets.py.

Remember to save the log file for later use!

• Run Federated Partial Averaging (FedPav):

  python main.py

• Run FedPav with knowledge distillation (KD):

  python main.py --kd --regularization

• Run FedPav with cosine distance weight (CDW):

  python main.py --cdw

• Run FedPav with knowledge distillation and cosine distance weight:

  python main.py --cdw --kd --regularization

@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}}@article{zhuang2023fedreid,  title={Optimizing performance of federated person re-identification: Benchmarking and analysis},  author={Zhuang, Weiming and Gan, Xin and Wen, Yonggang and Zhang, Shuai},  journal={ACM Transactions on Multimedia Computing, Communications and Applications},  volume={19},  number={1s},  pages={1--18},  year={2023},  publisher={ACM New York, NY}}

• Weiming Zhuang, Nanyang Technological University.
• Xin Gan, Nanyang Technological University.
• Daiying Yin, Nanyang Technological University. (Contributor)