FedSA: A Semi-Asynchronous Federated Learning Mechanism in Heterogeneous Edge Computing

Federated learning (FL) involves training machine learning models over distributed edge nodes ( i.e. , workers) while facing three critical challenges, edge heterogeneity, Non-IID data and communication resource constraint. In the synchronous FL, the parameter server has to wait for the slowest work...

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Veröffentlicht in:	IEEE journal on selected areas in communications Jg. 39; H. 12; S. 3654 - 3672
Hauptverfasser:	Ma, Qianpiao, Xu, Yang, Xu, Hongli, Jiang, Zhida, Huang, Liusheng, Huang, He
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.12.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Adaptive learning Algorithms Analytical models Cognitive tasks Collaborative work Completion time Computational modeling Convergence Data models Edge computing Federated learning Heterogeneity Machine learning Mathematical models non-IID Parameters semi-asynchronous mechanism Servers Staff participation Training data
ISSN:	0733-8716, 1558-0008
Online-Zugang:	Volltext
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