Can 5th Generation Local Training Methods Support Client Sampling? Yes!

29 December 2022

Papers citing "Can 5th Generation Local Training Methods Support Client Sampling? Yes!"

9 / 9 papers shown

Title
A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs Yan Sun Li Shen Dacheng Tao FedML 25 0 0 27 Sep 2024
Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation Haibo Yang Pei-Yuan Qiu Prashant Khanduri Minghong Fang Jia Liu FedML 40 1 0 04 May 2024
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression Laurent Condat A. Maranjyan Peter Richtárik 47 4 0 07 Mar 2024
DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime Jongho Park Jinchao Xu FedML 60 1 0 17 May 2023
Distributed Stochastic Optimization under a General Variance Condition Kun-Yen Huang Xiao Li Shin-Yi Pu FedML 40 6 0 30 Jan 2023
An Optimal Algorithm for Strongly Convex Min-min Optimization Alexander Gasnikov D. Kovalev Grigory Malinovsky 32 1 0 29 Dec 2022
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity A. Maranjyan M. Safaryan Peter Richtárik 34 13 0 28 Oct 2022
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 187 412 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