AHSecAgg and TSKG: Lightweight Secure Aggregation for Federated Learning Without Compromise

TL;DR

This paper introduces AHSecAgg, a lightweight secure aggregation protocol for federated learning that reduces computational costs while maintaining security, and TSKG, a threshold signature method that further enhances efficiency in fixed participant scenarios.

Contribution

The paper presents AHSecAgg, a novel lightweight secure aggregation protocol, and TSKG, an efficient key generation method, both improving security and reducing costs in federated learning.

Findings

AHSecAgg significantly reduces computation overhead compared to existing protocols.

TSKG eliminates secret sharing costs while maintaining security.

Experiments confirm improved efficiency and security in practical scenarios.

Abstract

Leveraging federated learning (FL) to enable cross-domain privacy-sensitive data mining represents a vital breakthrough to accomplish privacy-preserving learning. However, attackers can infer the original user data by analyzing the uploaded intermediate parameters during the aggregation process. Therefore, secure aggregation has become a critical issue in the field of FL. Many secure aggregation protocols face the problem of high computation costs, which severely limits their applicability. To this end, we propose AHSecAgg, a lightweight secure aggregation protocol using additive homomorphic masks. AHSecAgg significantly reduces computation overhead without compromising the dropout handling capability or model accuracy. We prove the security of AHSecAgg in semi-honest and active adversary settings. In addition, in cross-silo scenarios where the group of participants is relatively fixed during each round, we propose TSKG, a lightweight Threshold Signature based masking key generation method. TSKG can generate different temporary secrets and shares for different aggregation rounds using the initial key and thus effectively eliminates the cost of secret sharing and key agreement. We prove TSKG does not sacrifice security. Extensive experiments show that AHSecAgg significantly outperforms state-of-the-art mask-based secure aggregation protocols in terms of computational efficiency, and TSKG effectively reduces the computation and communication costs for existing secure aggregation protocols.