  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. Unfortunately, recent attacks on FL demonstrate that
local gradients may leak information about local training data. In response to
such attacks, Bonawitz \textit{et al.} (CCS 2017) proposed a secure aggregation
protocol that allows a server to compute the sum of clients' local gradients in
a secure manner. However, their secure aggregation protocol requires at least 4
rounds of communication between each client and the server in each training
iteration. The number of communication rounds is closely related not only to
the total communication cost but also the ML model accuracy, as the number of
communication rounds affects client dropouts.
  In this paper, we propose FSSA, a 3-round secure aggregation protocol, that
is efficient in terms of computation and communication, and resilient to client
dropouts. We prove the security of FSSA in honest-but-curious setting and show
that the security can be maintained even if an arbitrarily chosen subset of
clients drop out at any time. We evaluate the performance of FSSA and show that
its computation and communication overhead remains low even on large datasets.
Furthermore, we conduct an experimental comparison between FSSA and Bonawitz
\textit{et al.}'s protocol. The comparison results show that, in addition to
reducing the number of communication rounds, FSSA achieves a significant
improvement in computational efficiency.
