Group privacy for personalized federated learningDownload PDF

Published: 21 Oct 2022, Last Modified: 05 May 2023FL-NeurIPS 2022 OralReaders: Everyone
Keywords: federated learning, differential privacy, d-privacy, personalized models
Abstract: Federated learning exposes the participating clients to issues of leakage of private information from the client-server communication and the lack of personalization of the global model. To address both the problems, we investigate the use of metric-based local privacy mechanisms and model personalization. These are based on operations performed directly in the parameter space, i.e. sanitization of the model parameters by the clients and clustering of model parameters by the server.
Is Student: Yes
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