DP-SIGNSGD: When Efficiency Meets Privacy and Robustness

11 May 2021

Papers citing "DP-SIGNSGD: When Efficiency Meets Privacy and Robustness"

7 / 7 papers shown

Title
Compressed Private Aggregation for Scalable and Robust Federated Learning over Massive Networks Natalie Lang Nir Shlezinger Rafael G. L. DÓliveira S. E. Rouayheb FedML 65 4 0 01 Aug 2023
Joint Privacy Enhancement and Quantization in Federated Learning Natalie Lang Elad Sofer Tomer Shaked Nir Shlezinger FedML 27 46 0 23 Aug 2022
Dropout is NOT All You Need to Prevent Gradient Leakage Daniel Scheliga Patrick Mäder M. Seeland FedML 22 12 0 12 Aug 2022
Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam Yucheng Lu Conglong Li Minjia Zhang Christopher De Sa Yuxiong He OffRL AI4CE 22 20 0 12 Feb 2022
Robust and Privacy-Preserving Collaborative Learning: A Comprehensive Survey Shangwei Guo Xu Zhang Feiyu Yang Tianwei Zhang Yan Gan Tao Xiang Yang Liu FedML 18 9 0 19 Dec 2021
Privacy and Robustness in Federated Learning: Attacks and Defenses Lingjuan Lyu Han Yu Xingjun Ma Chen Chen Lichao Sun Jun Zhao Qiang Yang Philip S. Yu FedML 175 355 0 07 Dec 2020
Threats to Federated Learning: A Survey Lingjuan Lyu Han Yu Qiang Yang FedML 191 434 0 04 Mar 2020