  Secure aggregation is a popular protocol in privacy-preserving federated
learning, which allows model aggregation without revealing the individual
models in the clear. On the other hand, conventional secure aggregation
protocols incur a significant communication overhead, which can become a major
bottleneck in real-world bandwidth-limited applications. Towards addressing
this challenge, in this work we propose a lightweight gradient sparsification
framework for secure aggregation, in which the server learns the aggregate of
the sparsified local model updates from a large number of users, but without
learning the individual parameters. Our theoretical analysis demonstrates that
the proposed framework can significantly reduce the communication overhead of
secure aggregation while ensuring comparable computational complexity. We
further identify a trade-off between privacy and communication efficiency due
to sparsification. Our experiments demonstrate that our framework reduces the
communication overhead by up to 7.8x, while also speeding up the wall clock
training time by 1.13x, when compared to conventional secure aggregation
benchmarks.
