Faster Rates for Compressed Federated Learning with Client-Variance Reduction

24 December 2021

Papers citing "Faster Rates for Compressed Federated Learning with Client-Variance Reduction"

7 / 7 papers shown

Title
Federated Learning is Better with Non-Homomorphic Encryption Konstantin Burlachenko Abdulmajeed Alrowithi Fahad Ali Albalawi Peter Richtárik FedML 32 6 0 04 Dec 2023
Convergence and Privacy of Decentralized Nonconvex Optimization with Gradient Clipping and Communication Compression Boyue Li Yuejie Chi 21 12 0 17 May 2023
Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$ -Means Clustering Lingxiao Huang Zhize Li Jialin Sun Haoyu Zhao FedML 31 9 0 26 Oct 2022
FL_PyTorch: optimization research simulator for federated learning Konstantin Burlachenko Samuel Horváth Peter Richtárik FedML 29 18 0 07 Feb 2022
BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression Haoyu Zhao Boyue Li Zhize Li Peter Richtárik Yuejie Chi 19 48 0 31 Jan 2022
EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback Ilyas Fatkhullin Igor Sokolov Eduard A. Gorbunov Zhize Li Peter Richtárik 44 44 0 07 Oct 2021
A Field Guide to Federated Optimization Jianyu Wang Zachary B. Charles Zheng Xu Gauri Joshi H. B. McMahan ... Mi Zhang Tong Zhang Chunxiang Zheng Chen Zhu Wennan Zhu FedML 184 411 0 14 Jul 2021