FSSA: Efficient 3-Round Secure Aggregation for Privacy-Preserving
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.