Federated learning is a distributed framework for training machine learning
models over the data residing at mobile devices, while protecting the privacy
of individual users. A major bottleneck in scaling federated learning to a
large number of users is the overhead of secure model aggregation across many
users. In particular, the overhead of the state-of-the-art protocols for secure
model aggregation grows quadratically with the number of users. In this paper,
we propose the first secure aggregation framework, named Turbo-Aggregate, that
in a network with $N$ users achieves a secure aggregation overhead of
$O(Nlog{N})$, as opposed to $O(N^2)$, while tolerating up to a user dropout
rate of $50%$. Turbo-Aggregate employs a multi-group circular strategy for
efficient model aggregation, and leverages additive secret sharing and novel
coding techniques for injecting aggregation redundancy in order to handle user
dropouts while guaranteeing user privacy. We experimentally demonstrate that
Turbo-Aggregate achieves a total running time that grows almost linear in the
number of users, and provides up to $40times$ speedup over the
state-of-the-art protocols with up to $N=200$ users. Our experiments also
demonstrate the impact of model size and bandwidth on the performance of

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