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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) Jg. 2022; S. 10051 - 10061
Hauptverfasser:	Qu, Liangqiong, Zhou, Yuyin, Liang, Paul Pu, Xia, Yingda, Wang, Feifei, Adeli, Ehsan, Fei-Fei, Li, Rubin, Daniel
Format:	Tagungsbericht Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.06.2022
Schlagworte:	Computer architecture Data models Federated learning Organizations Privacy and federated learning Robustness Training Transformers
ISSN:	1063-6919, 1063-6919
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!