Server Averaging for Federated Learning

Federated learning allows distributed devices to collectively train a model without sharing or disclosing the local dataset with a central server. The global model is optimized by training and averaging the model parameters of all local participants. However, the improved privacy of federated learni...

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Veröffentlicht in:	arXiv.org 2021-03
Hauptverfasser:	Pu, George, Zhou, Yanlin, Wu, Dapeng, Li, Xiaolin
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Computation Convergence Federated learning Machine learning Servers
Online-Zugang:	Volltext
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