FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning

A Google TechTalk, 2020/7/30, presented by Swanand Kadhe, UC Berkeley ABSTRACT: Recent attacks on federated learning demonstrate that keeping the training data on clients' devices does not provide sufficient privacy, as the model parameters shared by clients can leak information about their training data. Therefore, it is crucial to develop ‘secure aggregation’ protocols that allow the server to aggregate client models in a privacy-preserving manner. In this work, we propose a secure aggregation protocol, FastSecAgg, that is efficient in terms of computation and communication, and robust to client dropouts. The main building block of FastSecAgg is a novel multi-secret sharing scheme, FastShare, based on the fast Fourier transform.