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Leveraging federated learning (FL) to enable cross-domain privacy-sensitivedata mining represents a vital breakthrough to accomplish privacy-preservinglearning. However, attackers can infer the original user data by analyzing theuploaded intermediate parameters during the aggregation process. Therefore,secure aggregation has become a critical issue in the field of FL. Many secureaggregation protocols face the problem of high computation costs, whichseverely limits their applicability. To this end, we propose AHSecAgg, alightweight secure aggregation protocol using additive homomorphic masks.AHSecAgg significantly reduces computation overhead without compromising thedropout handling capability or model accuracy. We prove the security ofAHSecAgg in semi-honest and active adversary settings. In addition, incross-silo scenarios where the group of participants is relatively fixed duringeach round, we propose TSKG, a lightweight Threshold Signature based maskingkey generation method. TSKG can generate different temporary secrets and sharesfor different aggregation rounds using the initial key and thus effectivelyeliminates the cost of secret sharing and key agreement. We prove TSKG does notsacrifice security. Extensive experiments show that AHSecAgg significantlyoutperforms state-of-the-art mask-based secure aggregation protocols in termsof computational efficiency, and TSKG effectively reduces the computation andcommunication costs for existing secure aggregation protocols.

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