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May, 2022
具有服务器端信息的个性化联邦学习
Personalized Federated Learning with Server-Side Information
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Jaehun Song, Min-hwan Oh, Hyung-Sin Kim
TL;DR
研究个性化联邦学习中如何利用服务器数据来提高meta-gradient计算以增加个性化性能的FedSIM方法,并通过各种基准测试和消融实验证明其优于现有方法,不仅计算效率更高,而且收敛速度高达34.2%。
Abstract
personalized federated learning
(FL) is an emerging research field in FL that learns an easily adaptable global model in the presence of
data heterogeneity
among clients. However, one of the main challenges for p
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