SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning

We propose
SwiftAgg+
, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of
$N \in \mathbb {N}$
distributed users, each of size
$L \in \mathbb {N}$
, trained on their local data, in a privacy-preserving manner.
SwiftAgg+
can significantly reduce the communication overheads without any compromise on security, and achieve optimal communication loads within diminishing gaps. Specifically, in presence of at most
$D=o(N)$
dropout users,
SwiftAgg+
achieves a per-user communication load of
$\left({1+\mathcal {O}\left({\frac {1}{N}}\right)}\right)L$
symbols and a server communication load of
$\left({1+\mathcal {O}\left({\frac {1}{N}}\right)}\right)L$
symbols, with a worst-case information-theoretic security guarantee, against any subset of up to
$T=o(N)$
semi-honest users who may also collude with the curious server. Moreover, the proposed
SwiftAgg+
allows for a flexible trade-off between communication loads and the number of active communication links. In particular, for
$T< N-D$
and for any
$K\in \mathbb {N}$
,
SwiftAgg+
can achieve the server communication load of
$\left({1+\frac {T}{K}}\right)L$
symbols, and per-user communication load of up to
$\left({1+\frac {T+D}{K}}\right)L$
symbols, where the number of pair-wise active connections in the network is
$\frac {N}{2}(K+T+D+1)$
.