Standing Firm in 5G: A Single-Round, Dropout-Resilient Secure Aggregation for Federated Learning

TL;DR

This paper introduces a lightweight, single-round secure aggregation protocol tailored for 5G federated learning, addressing challenges of device dropouts and mobility while enhancing privacy, efficiency, and robustness.

Contribution

It presents a novel secure aggregation protocol that is single-round, dropout-resilient, and leverages base stations, precomputation, and secret sharing for practical 5G FL deployment.

Findings

Strong security guarantees demonstrated

Significant communication efficiency gains

Robustness against device dropouts in 5G environments

Abstract

Federated learning (FL) is well-suited to 5G networks, where many mobile devices generate sensitive edge data. Secure aggregation protocols enhance privacy in FL by ensuring that individual user updates reveal no information about the underlying client data. However, the dynamic and large-scale nature of 5G-marked by high mobility and frequent dropouts-poses significant challenges to the effective adoption of these protocols. Existing protocols often require multi-round communication or rely on fixed infrastructure, limiting their practicality. We propose a lightweight, single-round secure aggregation protocol designed for 5G environments. By leveraging base stations for assisted computation and incorporating precomputation, key-homomorphic pseudorandom functions, and t-out-of-k secret sharing, our protocol ensures efficiency, robustness, and privacy. Experiments show strong security guarantees and significant gains in communication and computation efficiency, making the approach well-suited for real-world 5G FL deployments.