Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning

Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution. Despite recent progress, there remain fundamental challenges such as the lack of convergence and the potential...

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Vydané v:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) Ročník 2022; s. 10051 - 10061
Hlavní autori:	Qu, Liangqiong, Zhou, Yuyin, Liang, Paul Pu, Xia, Yingda, Wang, Feifei, Adeli, Ehsan, Fei-Fei, Li, Rubin, Daniel
Médium:	Konferenčný príspevok.. Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.06.2022
Predmet:	Computer architecture Data models Federated learning Organizations Privacy and federated learning Robustness Training Transformers
ISSN:	1063-6919, 1063-6919
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