Resource Allocation of NOMA Communication Systems for Federated Learning

Federated learning is a new communication and computing concept that allows naturally distributed data sets (e.g., as in data acquisition sensors) to be used to train global models, and therefore successfully addresses privacy, power and bandwidth limitations in wireless networks. In this paper, we...

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Veröffentlicht in:	IEEE communications letters Jg. 27; H. 8; S. 1
Hauptverfasser:	Poposka, Marija, Jovanovski, Borche, Rakovic, Valentin, Denkovski, Daniel, Hadzi-Velkov, Zoran
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Communications systems Computational modeling Data acquisition Data models Exact solutions Federated learning latency minimization Machine learning Mathematical models Maximum power Network latency NOMA non-orthogonal multiple access Nonorthogonal multiple access Numerical models Optimization Parameters Resource allocation Resource management Uplink wireless federated learning Wireless networks Wireless sensor networks
ISSN:	1089-7798, 1558-2558
Online-Zugang:	Volltext
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