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

• URL: http://arxiv.org/abs/2203.13060v1
• Date: Thu, 24 Mar 2022 13:12:23 GMT
• Title: SwiftAgg+: Achieving Asymptotically Optimal Communication Load in Secure Aggregation for Federated Learning
• Authors: Tayyebeh Jahani-Nezhad, Mohammad Ali Maddah-Ali, Songze Li, Giuseppe Caire
• Abstract summary: SwiftAgg+ is a novel secure aggregation protocol for federated learning systems. A central server aggregates local models of $NinmathbbN$ distributed users, each of size $L in mathbbN$, trained on their local data, in a privacy-preserving manner.
• Score: 83.94234859890402
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 the optimum communication load within a diminishing gap. Specifically, in presence of at most $D$ dropout users, SwiftAgg+ achieves average per-user communication load of $(1+\mathcal{O}(\frac{1}{N}))L$ and the server communication load of $(1+\mathcal{O}(\frac{1}{N}))L$, 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 proposed SwiftAgg+ has also a flexibility to reduce the number of active communication links at the cost of increasing the the communication load between the users and the server. In particular, for any $K\in\mathbb{N}$, SwiftAgg+ can achieve the uplink communication load of $(1+\frac{T}{K})L$, and per-user communication load of up to $(1-\frac{1}{N})(1+\frac{T+D}{K})L$, where the number of pair-wise active connections in the network is $\frac{N}{2}(K+T+D+1)$.

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