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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Bibliographic Details

Published in:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) Vol. 2022; pp. 10051 - 10061
Main Authors:	Qu, Liangqiong, Zhou, Yuyin, Liang, Paul Pu, Xia, Yingda, Wang, Feifei, Adeli, Ehsan, Fei-Fei, Li, Rubin, Daniel
Format:	Conference Proceeding Journal Article
Language:	English
Published:	United States IEEE 01.06.2022
Subjects:	Computer architecture Data models Federated learning Organizations Privacy and federated learning Robustness Training Transformers
ISSN:	1063-6919, 1063-6919
Online Access:	Get full text
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