A Lightweight Secure Aggregation Protocol for Federated Learning Applications

Federated learning (FL) enables a server and a set of users to jointly train a model without the need to disclose training data. To guarantee strong privacy for users, FL must be combined with a secure aggregation (SA) protocol ensuring that individual gradients remain confidential. Pioneering SA protocols for FL applications exhibit a per-user overhead that grows logarithmically with the number of users and require multiple rounds of communication.In this paper we present LISA, a lightweight SA protocol that leverages public randomness to reduce both per-user overhead and the number of rounds needed. LISA requires only two rounds of interaction for most users, offering a communication overhead asymptotically equivalent to that of a non-private protocol where the server obtains user inputs in the clear. More concretely, LISA uses public randomness to select a small committee of users tasked with aiding the server in the aggregation process. Users blind their individual inputs with random masks shared with each committee member, and after sending their blinded input to the server they can go offline. Committee members provide the server with an aggregation of the shared masks over all users, so that the server can remove all masks and obtain the aggregated input. Hence, as long as one committee member is honest, the server can only obtain the aggregation of threshold-many inputs without learning any individual input. We compare LISA with existing SA protocols through both theoretical analysis and simulations. We further integrate LISA in an FL pipeline and compare its accuracy and time to converge with those of a non-private FL application.