CMFL: Mitigating Communication Overhead for Federated Learning

Federated Learning enables mobile users to collaboratively learn a global prediction model by aggregating their individual updates without sharing the privacy-sensitive data. As mobile devices usually have limited data plan and slow network connections to the central server where the global model is...

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Veröffentlicht in:	Proceedings of the International Conference on Distributed Computing Systems S. 954 - 964
Hauptverfasser:	WANG, Luping, WANG, Wei, LI, Bo
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.07.2019
Schlagworte:	Communication efficiency Computational modeling Convergence Data models Federated Learning Network Optimization Servers Training Training data
ISSN:	2575-8411
Online-Zugang:	Volltext
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