Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation

Federated learning is a setting where agents, each with access to their own data source, combine models from local data to create a global model. If agents are drawing their data from different distributions, though, federated learning might produce a biased global model that is not optimal for each...

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Veröffentlicht in:	arXiv.org 2020-12
Hauptverfasser:	Donahue, Kate, Kleinberg, Jon
Format:	Artikel
Sprache:	eng
Schlagworte:	Federated learning Game theory Heterogeneity Mathematical models Parameters Players Regression analysis
Online-Zugang:	Volltext
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