Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air

We study federated machine learning (ML) at the wireless edge, where power- and bandwidth-limited wireless devices with local datasets carry out distributed stochastic gradient descent (DSGD) with the help of a parameter server (PS). Standard approaches assume separate computation and communication,...

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Veröffentlicht in:	IEEE transactions on signal processing Jg. 68; S. 2155 - 2169
Hauptverfasser:	Mohammadi Amiri, Mohammad, Gunduz, Deniz
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Approximate message passing (AMP) Bandwidth Bandwidths Channel estimation Computation Convergence Electronic devices Estimates federated learning (FL) Machine learning Machine learning algorithms over-the-air computation Performance evaluation Power management Quantization (signal) Robustness (mathematics) stochastic gradient descent (SGD) Wireless communication Wireless sensor networks
ISSN:	1053-587X, 1941-0476
Online-Zugang:	Volltext
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