Towards Federated Learning Under Resource Constraints via Layer-wise Training and Depth Dropout

Large machine learning models trained on diverse data have recently seen unprecedented success. Federated learning enables training on private data that may otherwise be inaccessible, such as domain-specific datasets decentralized across many clients. However, federated learning can be difficult to...

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Veröffentlicht in:	arXiv.org 2023-09
Hauptverfasser:	Guo, Pengfei, Morningstar, Warren Richard, Vemulapalli, Raviteja, Singhal, Karan, Patel, Vishal M, Mansfield, Philip Andrew
Format:	Artikel
Sprache:	eng
Schlagworte:	Clients Federated learning Machine learning Performance degradation Self-supervised learning
Online-Zugang:	Volltext
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