SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning
Tayyebeh Jahani-Nezhad, Mohammad Ali Maddah-Ali, Songze Li, Giuseppe Caire · IEEE Journal on Selected Areas in Communications · 2023
We proposeSwiftAgg+, 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 proposedSwiftAgg+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$\frac {N}{2}(K+T+D+1)$.