LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning

TL;DR

LightSecAgg introduces a new secure aggregation protocol for federated learning that reduces overhead, supports asynchronous settings, and accelerates training through modular design and parallelization.

Contribution

It proposes LightSecAgg, a novel aggregation scheme that simplifies dropout-resilient privacy-preserving federated learning with improved efficiency and scalability.

Findings

Reduces overhead for dropout resilience compared to state-of-the-art protocols.

Supports asynchronous federated learning environments.

Significantly decreases total training time in large-scale experiments.

Abstract

Secure model aggregation is a key component of federated learning (FL) that aims at protecting the privacy of each user's individual model while allowing for their global aggregation. It can be applied to any aggregation-based FL approach for training a global or personalized model. Model aggregation needs to also be resilient against likely user dropouts in FL systems, making its design substantially more complex. State-of-the-art secure aggregation protocols rely on secret sharing of the random-seeds used for mask generations at the users to enable the reconstruction and cancellation of those belonging to the dropped users. The complexity of such approaches, however, grows substantially with the number of dropped users. We propose a new approach, named LightSecAgg, to overcome this bottleneck by changing the design from "random-seed reconstruction of the dropped users" to "one-shot aggregate-mask reconstruction of the active users via mask encoding/decoding". We show that LightSecAgg achieves the same privacy and dropout-resiliency guarantees as the state-of-the-art protocols while significantly reducing the overhead for resiliency against dropped users. We also demonstrate that, unlike existing schemes, LightSecAgg can be applied to secure aggregation in the asynchronous FL setting. Furthermore, we provide a modular system design and optimized on-device parallelization for scalable implementation, by enabling computational overlapping between model training and on-device encoding, as well as improving the speed of concurrent receiving and sending of chunked masks. We evaluate LightSecAgg via extensive experiments for training diverse models on various datasets in a realistic FL system with large number of users and demonstrate that LightSecAgg significantly reduces the total training time.