Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning

TL;DR

Turbo-Aggregate introduces a novel secure federated learning aggregation method that reduces overhead from quadratic to near-linear growth, enabling scalable privacy-preserving model training across many users.

Contribution

It presents the first secure aggregation protocol with $O(N ext{log}N)$ complexity, employing multi-group strategies and coding techniques to handle user dropouts efficiently.

Findings

Achieves near-linear growth in total running time with the number of users.

Provides up to 40x speedup over previous protocols for 200 users.

Maintains privacy and dropout tolerance up to 50%.

Abstract

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 $O(N\log{N})$, 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 $40\times$ 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 Turbo-Aggregate.