Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing

Federated Learning (FL) is an emerging approach for collaboratively training Deep Neural Networks (DNNs) on mobile devices, without private user data leaving the devices. Previous works have shown that non-Independent and Identically Distributed (non-IID) user data harms the convergence speed of the...

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Veröffentlicht in:	IEEE transactions on parallel and distributed systems Jg. 33; H. 3; S. 630 - 641
Hauptverfasser:	Mills, Jed, Hu, Jia, Min, Geyong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.03.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models adaptive optimization Algorithms Artificial neural networks Computational modeling Convergence Customization Data models deep learning Edge computing Electronic devices Federated learning Iterative methods Machine learning Model accuracy multi-task learning Neural networks Optimization Servers Training
ISSN:	1045-9219, 1558-2183
Online-Zugang:	Volltext
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