Models of fairness in federated learning

In many real-world situations, data is distributed across multiple self-interested agents. These agents can collaborate to build a machine learning model based on data from multiple agents, potentially reducing the error each experiences. However, sharing models in this way raises questions of fairn...

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Bibliographische Detailangaben

Hauptverfasser:	Donahue, Kate, Kleinberg, Jon
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Computer Science and Game Theory Computer Science - Computers and Society Computer Science - Distributed, Parallel, and Cluster Computing Computer Science - Learning
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