Federated Learning Using Three-Operator ADMM

Federated learning (FL) has emerged as an instance of distributed machine learning paradigm that avoids the transmission of data generated on the users' side. Although data are not transmitted, edge devices have to deal with limited communication bandwidths, data heterogeneity, and straggler ef...

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Veröffentlicht in:	IEEE journal of selected topics in signal processing Jg. 17; H. 1; S. 205 - 221
Hauptverfasser:	Kant, Shashi, Silva, Jose Mairton B. da, Fodor, Gabor, Goransson, Bo, Bengtsson, Mats, Fischione, Carlo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Base stations communication efficiency Computer Science with specialization in Computer Communication Convergence Cost function Data models Datasets Datavetenskap med inriktning mot datorkommunikation distributed machine learning Federated learning Heterogeneity Machine learning Maskininlärning Numerical models Optimization Servers Signal processing algorithms three-operator ADMM
ISSN:	1932-4553, 1941-0484, 1941-0484
Online-Zugang:	Volltext
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