Group privacy for personalized federated learning

Filippo Galli, Sayan Biswas, Kangsoo Jung, Tommaso Cucinotta, Catuscia Palamidessi

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