Federated Learning with Local Differential Privacy

Citation

If you find "federated learning with local DP" useful in your research, please consider citing:

@ARTICLE{kang2020fed,
author={Wei, Kang and Li, Jun and Ding, Ming and Ma, Chuan and Yang, Howard H. and Farokhi, Farhad and Jin, Shi and Quek, Tony Q. S. and Poor, H. Vincent},
journal={IEEE Transactions on Information Forensics and Security}, 
title={Federated Learning With Differential Privacy: {Algorithms} and Performance Analysis}, 
year={2020},
volume={15},
number={},
pages={3454-3469},}

@ARTICLE{Wei2021User,
author={K. {Wei} and J. {Li} and M. {Ding} and C. {Ma} and H. {Su} and B. {Zhang} and H. V. {Poor}},
journal={IEEE Transactions on Mobile Computing}, 
title={User-Level Privacy-Preserving Federated Learning: {Analysis} and Performance Optimization}, 
year={2021},
volume={},
number={},
pages={1-1},}}

@ARTICLE{Ma202On,
author={C. {Ma} and J. {Li} and M. {Ding} and H. H. {Yang} and F. {Shu} and T. Q. S. {Quek} and H. V. {Poor}},
title={On Safeguarding Privacy and Security in the Framework of Federated Learning},
journal   = {{IEEE} Network},
volume    = {34},
number    = {4},
pages     = {242-248},
year      = {2020},}

Prerequisites

Python 3.6
Torch 1.5.1

Models&Data

Learning models: CNN, MLP and SVM
Datasets: Mnist and Adult

Training

Description