Robust Personalized Federated Learning with Sparse Penalization*

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Weidong Liu, Xiaojun Mao, Xiaofei Zhang, Xin Zhang
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引用次数: 0

Abstract

Federated learning (FL) is an emerging topic due to its advantage in collaborative learning with distributed data. Due to the heterogeneity in the local data-generating mechanism, it is important t...
利用稀疏惩罚进行稳健的个性化联合学习*
联邦学习(FL)因其在分布式数据协作学习方面的优势而成为一个新兴课题。由于本地数据生成机制存在异质性,因此必须对其进行优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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