  We propose SwiftAgg, a novel secure aggregation protocol for federated
learning systems, where a central server aggregates local models of $N$
distributed users, each of size $L$, trained on their local data, in a
privacy-preserving manner. Compared with state-of-the-art secure aggregation
protocols, SwiftAgg significantly reduces the communication overheads without
any compromise on security. Specifically, in presence of at most $D$ dropout
users, SwiftAgg achieves a users-to-server communication load of $(T+1)L$ and a
users-to-users communication load of up to $(N-1)(T+D+1)L$, with a worst-case
information-theoretic security guarantee, against any subset of up to $T$
semi-honest users who may also collude with the curious server. The key idea of
SwiftAgg is to partition the users into groups of size $D+T+1$, then in the
first phase, secret sharing and aggregation of the individual models are
performed within each group, and then in the second phase, model aggregation is
performed on $D+T+1$ sequences of users across the groups. If a user in a
sequence drops out in the second phase, the rest of the sequence remain silent.
This design allows only a subset of users to communicate with each other, and
only the users in a single group to directly communicate with the server,
eliminating the requirements of 1) all-to-all communication network across
users; and 2) all users communicating with the server, for other secure
aggregation protocols. This helps to substantially slash the communication
costs of the system.
