  Secure aggregation is commonly used in federated learning (FL) to alleviate
privacy concerns related to the central aggregator seeing all parameter updates
in the clear. Unfortunately, most existing secure aggregation schemes ignore
two critical orthogonal research directions that aim to (i) significantly
reduce client-server communication and (ii) mitigate the impact of malicious
clients. However, both of these additional properties are essential to
facilitate cross-device FL with thousands or even millions of (mobile)
participants.
  In this paper, we unite both research directions by introducing ScionFL, the
first secure aggregation framework for FL that operates efficiently on
quantized inputs and simultaneously provides robustness against malicious
clients. Our framework leverages (novel) multi-party computation (MPC)
techniques and supports multiple linear (1-bit) quantization schemes, including
ones that utilize the randomized Hadamard transform and Kashin's
representation.
  Our theoretical results are supported by extensive evaluations. We show that
with no overhead for clients and moderate overhead for the server compared to
transferring and processing quantized updates in plaintext, we obtain
comparable accuracy for standard FL benchmarks. Moreover, we demonstrate the
robustness of our framework against state-of-the-art poisoning attacks.
