Federated_learning_with_differential_privacy：使用PyTorch通过各种神经网络和s...

Federated Learning with Differential Privacy ====== Citation ----- If you find "federated learning with DP" useful in your research, please consider citing: @ARTICLE{Wei2020Fed, author={Kang Wei and Jun Li and Ming Ding and Chuan Ma and Howard H. Yang and Farhad Farokhi and Shi Jin and Tony Q. S. Quek and H. Vincent Poor}, 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{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 ----- Training ----- Description -----