FSSA: Efficient 3-Round Secure Aggregation for Privacy-Preserving Federated Learning

Federated learning (FL) allows a large number of clients to collaboratively train machine learning (ML) models by sending only their local gradients to a central server for aggregation in each training iteration, without sending their raw training data.  This paper proposes a 3-round secure aggregation protocol, that is effificient in terms of computation and communication, and resilient to client dropouts.