Adaptive Federated Learning in Resource Constrained Edge Computing Systems

Emerging technologies and applications including Internet of Things, social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Du...

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Veröffentlicht in:	IEEE journal on selected areas in communications Jg. 37; H. 6; S. 1205 - 1221
Hauptverfasser:	Wang, Shiqiang, Tuor, Tiffany, Salonidis, Theodoros, Leung, Kin K., Makaya, Christian, He, Ting, Chan, Kevin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.06.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptive systems Algorithms Artificial intelligence Computer simulation Control algorithms Control theory Convergence Data models Distributed databases Distributed machine learning Edge computing Experimentation Federated learning Machine learning Machine learning algorithms Mathematical models mobile edge computing New technology Parameters Peer-to-peer computing Training wireless networking
ISSN:	0733-8716, 1558-0008
Online-Zugang:	Volltext
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