Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning

9 July 2022

Papers citing "Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning"

8 / 8 papers shown

Title
Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection Jiahao Xu Zikai Zhang Rui Hu AAML FedML Presented at ResearchTrend Connect \| FedML on 28 Mar 2025 145 0 0 11 Mar 2025
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression Laurent Condat A. Maranjyan Peter Richtárik 39 3 0 07 Mar 2024
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates Siqi Zhang S. Choudhury Sebastian U. Stich Nicolas Loizou FedML 19 3 0 08 Jun 2023
DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime Jongho Park Jinchao Xu FedML 47 1 0 17 May 2023
Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities Aleksandr Beznosikov Martin Takáč Alexander Gasnikov 21 10 0 15 Feb 2023
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 175 411 0 14 Jul 2021
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients A. Mitra Rayana H. Jaafar George J. Pappas Hamed Hassani FedML 55 157 0 14 Feb 2021
Linearly Converging Error Compensated SGD Eduard A. Gorbunov D. Kovalev Dmitry Makarenko Peter Richtárik 163 77 0 23 Oct 2020