DMSA-FL: secure and robust federated learning via double-masked secure aggregation with multiparty homomorphic encryption

Abstract

Privacy preserving federated learning (FL) faces a crucial trade-off between privacy and efficiency. Specifically, traditional multi-party homomorphic encryption incurs high computational and communication costs, while schemes relying on a single mask are susceptible to selective aggregation attacks. To address these limitations, this paper proposes DMSA-FL as a practical and robust FL framework leveraging double-masked secure aggregation integrated with multiparty homomorphic encryption. We mitigate efficiency bottlenecks by designing a strategy that encrypts only low-dimensional mask seeds rather than high-dimensional gradients. This method utilizes a homomorphic pseudorandom generator for mask expansion. Furthermore, a robustness protocol based on threshold secret sharing over sparse sketches is implemented to ensure aggregation continuity during user dropouts. Theoretical analysis demonstrates that DMSA-FL is secure against collusion attacks and selective aggregation attacks. Extensive experiments on multiple datasets indicate that DMSA-FL maintains model accuracy comparable to plaintext baselines. Compared with representative federated learning schemes based on CKKS multiparty homomorphic encryption, our approach achieves improved computational efficiency and significantly reduces communication overhead. Moreover, the computational advantage becomes increasingly evident as the number of model parameters grows.