Federated-Learning-with-Local-Differential-Privacy-master.zip

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