Official repository for WWW 2022 paper An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning. This project is developed based on Python 3.6.
pip install -r requirements.txt
Download the "" [download link] and run 'unzip dataset.zip' at the root directory before training.
All code for this part are included in "UMBD" and "ABD" subfolder. Please change to that folder before running the code.
- Train models with PBPFL
bash pbpfl_train.sh
- Train models with traditional AVGFL
bash avgfl_train.sh
All code for this part are included in "LDC" subfolder. Please change to that folder before running the code.
- Train models with PBPFL
bash pbpfl_train.sh
- Train models with traditional AVGFL on MSE loss
bash avgfl_mse_train.sh
- Train models with traditional AVGFL on CE loss
bash avgfl_ce_train.sh
Please cite our paper in your publications if it helps your research:
@inproceedings{Yang_WWW_2022,
title={An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning},
author={Xue Yang, Yan Feng, Weijun Fang, Jun Shao, Xiaohu Tang, Shu-Tao Xia, Rongxing Lu},
booktitle={Proceedings of the ACM Web Conference 2022},
year={2022}
}