Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT

The rapidly expanding number of Internet of Things (IoT) devices is generating huge quantities of data, but public concern over data privacy means users are apprehensive to send data to a central server for machine learning (ML) purposes. The easily changed behaviors of edge infrastructure that soft...

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Veröffentlicht in:	IEEE internet of things journal Jg. 7; H. 7; S. 5986 - 5994
Hauptverfasser:	Mills, Jed, Hu, Jia, Min, Geyong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.07.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Algorithms Clients Communication Compression Computational modeling Convergence Data models Datasets distributed computing Edge computing Federated learning federated learning (FL) Internet of Things Internet of Things (IoT) Logic gates Machine learning Optimization Servers Software-defined networking Training Wireless communications
ISSN:	2327-4662, 2327-4662
Online-Zugang:	Volltext
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