Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise

17 July 2020

Papers citing "Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise"

8 / 8 papers shown

Title
Asynchronous Byzantine Federated Learning Bart Cox Abele Malan Lydia Y. Chen Jérémie Decouchant 50 1 0 03 Jun 2024
Time-triggered Federated Learning over Wireless Networks Xiaokang Zhou Yansha Deng Huiyun Xia Shaochuan Wu M. Bennis FedML 37 20 0 26 Apr 2022
Asynchronous Collaborative Learning Across Data Silos Tiffany Tuor J. Lockhart Daniele Magazzeni FedML 34 3 0 23 Mar 2022
FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations Jinhyun So Kevin Hsieh Behnaz Arzani Shadi Noghabi Salman Avestimehr Ranveer Chandra FedML 16 60 0 02 Feb 2022
Asynchronous Federated Learning on Heterogeneous Devices: A Survey Chenhao Xu Youyang Qu Yong Xiang Longxiang Gao FedML 104 241 0 09 Sep 2021
Federated Learning with Buffered Asynchronous Aggregation John Nguyen Kshitiz Malik Hongyuan Zhan Ashkan Yousefpour Michael G. Rabbat Mani Malek Dzmitry Huba FedML 33 288 0 11 Jun 2021
Federated Learning: Opportunities and Challenges P. Mammen FedML 48 211 0 14 Jan 2021
New Convergence Aspects of Stochastic Gradient Algorithms Lam M. Nguyen Phuong Ha Nguyen Peter Richtárik K. Scheinberg Martin Takáč Marten van Dijk 23 66 0 10 Nov 2018