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On the Convergence of Federated Averaging with Cyclic Client
  Participation

On the Convergence of Federated Averaging with Cyclic Client Participation

6 February 2023
Yae Jee Cho
Pranay Sharma
Gauri Joshi
Zheng Xu
Satyen Kale
Tong Zhang
    FedML
ArXivPDFHTML

Papers citing "On the Convergence of Federated Averaging with Cyclic Client Participation"

9 / 9 papers shown
Title
Convergence Analysis of Asynchronous Federated Learning with Gradient Compression for Non-Convex Optimization
Convergence Analysis of Asynchronous Federated Learning with Gradient Compression for Non-Convex Optimization
Diying Yang
Yingwei Hou
Danyang Xiao
Weigang Wu
FedML
39
0
0
28 Apr 2025
Save It All: Enabling Full Parameter Tuning for Federated Large Language
  Models via Cycle Block Gradient Descent
Save It All: Enabling Full Parameter Tuning for Federated Large Language Models via Cycle Block Gradient Descent
Lin Wang
Zhichao Wang
Xiaoying Tang
42
1
0
17 Jun 2024
Understanding Server-Assisted Federated Learning in the Presence of
  Incomplete Client Participation
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
Distributed Personalized Empirical Risk Minimization
Distributed Personalized Empirical Risk Minimization
Yuyang Deng
Mohammad Mahdi Kamani
Pouria Mahdavinia
M. Mahdavi
31
4
0
26 Oct 2023
Papaya: Practical, Private, and Scalable Federated Learning
Papaya: Practical, Private, and Scalable Federated Learning
Dzmitry Huba
John Nguyen
Kshitiz Malik
Ruiyu Zhu
Michael G. Rabbat
...
H. Srinivas
Kaikai Wang
Anthony Shoumikhin
Jesik Min
Mani Malek
FedML
110
137
0
08 Nov 2021
A Field Guide to Federated Optimization
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
411
0
14 Jul 2021
Practical and Private (Deep) Learning without Sampling or Shuffling
Practical and Private (Deep) Learning without Sampling or Shuffling
Peter Kairouz
Brendan McMahan
Shuang Song
Om Thakkar
Abhradeep Thakurta
Zheng Xu
FedML
182
154
0
26 Feb 2021
Federated Evaluation and Tuning for On-Device Personalization: System
  Design & Applications
Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications
Matthias Paulik
M. Seigel
Henry Mason
Dominic Telaar
Joris Kluivers
...
Dominic Hughes
O. Javidbakht
Fei Dong
Rehan Rishi
Stanley Hung
FedML
177
126
0
16 Feb 2021
Linear Convergence of Gradient and Proximal-Gradient Methods Under the
  Polyak-Łojasiewicz Condition
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition
Hamed Karimi
J. Nutini
Mark W. Schmidt
139
1,199
0
16 Aug 2016
1