description

This is the code for Securing Distributed SGD against Gradient Leakage Threats, which is published at IEEE Transactions on Parallel and Distributed Systems in 2023.

For Gradient leakage resilient federated learning

• Go to DP_code folder

• First install the conda environment with environment.yml.

• Then run create_FLdistribution.sh to create a client distribution first (each client has two shards). The distribution file xxx_1000_clients.pkl would appear in the client folder and you may run the rest.

• This code includes both the participation-level Fed-SDP (FedSDP.py), instance-level Fed-CDP baseline (Fed_CDP_fixed.py) and Fed-DP dynamic(Fed_CDP_dynamic.py) . You may adjust the few lines within the local training for l2-max sensitivity (default fixed clipping sensitivity) and dynamic noise scale (default fixed noise scale). The privacy accounting approach is also provided for $\epsilon$ privacy spending in Fed-CDP.

For leakage attacks

Go to gradient_leakage/ folder for gradient leakage attacks on MNIST, Fashion-MNIST, CIFAR10 and LFW (both type 1 leakage and type 2 leakage).

If you are interested in our research, please cite:

@inproceedings{wei2020framework,
  title={A framework for evaluating client privacy leakages in federated learning},
  author={Wei, Wenqi and Liu, Ling and Loper, Margaret and Chow, Ka-Ho and Gursoy, Mehmet Emre and Truex, Stacey and Wu, Yanzhao},
  booktitle={European Symposium on Research in Computer Security},
  year={2020},
 publisher={Springer}
}

@inproceedings{wei2021gradient,
  title={Gradient-Leakage Resilient Federated Learning},
  author={Wei, Wenqi and Liu, Ling and Wu, Yanzhao and Su, Gong and Iyengar, Arun},
booktitle={International Conference on Distributed Computing Systems},
  year={2021},
 publisher={IEEE}}


@ARTICLE{wei2022gradient,
  author={Wei, Wenqi and Liu, Ling},
  journal={IEEE Transactions on Information Forensics and Security}, 
  title={Gradient Leakage Attack Resilient Deep Learning}, 
  year={2022},
  volume={17},
  number={},
  pages={303-316},
  doi={10.1109/TIFS.2021.3139777}}


@ARTICLE{wei2023securing,
  author={Wei, Wenqi and Liu, Ling and Zhou, Jingya and Chow, Ka-Ho and Wu, Yanzhao},
  journal={IEEE Transactions on Parallel and Distributed Systems}, 
  title={Securing Distributed SGD Against Gradient Leakage Threats}, 
  year={2023},
  volume={34},
  number={7},
  pages={2040-2054},
  doi={10.1109/TPDS.2023.3273490}}


}