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Momentum Benefits Non-IID Federated Learning Simply and Provably
v1v2v3 (latest)

Momentum Benefits Non-IID Federated Learning Simply and Provably

28 June 2023
Ziheng Cheng
Xinmeng Huang
Pengfei Wu
Kun Yuan
    FedML
ArXiv (abs)PDFHTML

Papers citing "Momentum Benefits Non-IID Federated Learning Simply and Provably"

4 / 4 papers shown
Title
Optimized Local Updates in Federated Learning via Reinforcement Learning
Optimized Local Updates in Federated Learning via Reinforcement Learning
Ali Murad
Bo Hui
Wei-Shinn Ku
FedML
44
0
0
31 May 2025
DBA-DFL: Towards Distributed Backdoor Attacks with Network Detection in Decentralized Federated Learning
DBA-DFL: Towards Distributed Backdoor Attacks with Network Detection in Decentralized Federated Learning
Bohan Liu
Yang Xiao
Ruimeng Ye
Zinan Ling
Xiaolong Ma
Bo Hui
OODAAMLFedML
113
1
0
25 Jan 2025
Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression
Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression
Yutong He
Xinmeng Huang
Yiming Chen
W. Yin
Kun Yuan
113
7
0
12 May 2023
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning
Sai Praneeth Karimireddy
Martin Jaggi
Satyen Kale
M. Mohri
Sashank J. Reddi
Sebastian U. Stich
A. Suresh
FedML
176
219
0
08 Aug 2020
1