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Dec, 2023
关于服务器动量在联邦学习中的作用
On the Role of Server Momentum in Federated Learning
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Jianhui Sun, Xidong Wu, Heng Huang, Aidong Zhang
TL;DR
提出了一种服务器动量的通用框架,用来解决联邦学习中由于客户端系统和数据异质性引起的收敛问题,并通过严密的收敛分析和大量实验证实了该框架的有效性。
Abstract
federated averaging
(FedAvg) is known to experience
convergence issues
when encountering significant clients system
heterogeneity
and data
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