Secure and Efficient Federated Learning via Novel Authenticable Multi-Party Computation and Compressed Sensing

Abstract

Federated learning (FL) facilitates collaborative training of a global model without sharing the participants’ raw data. Nevertheless, existing FL approaches still face three major issues: 1) How to propose a more efficient and secure privacy-preserving method; 2) How to verify the identity of participants to ensure they are not impersonators; 3) How to reduce the significant communication cost. To address the aforementioned concerns, several schemes have been proposed. However, these schemes suffer from flaws in security, efficiency, and functionality. Furthermore, few researches have considered the possibility of adversaries impersonating legitimate participants to undermine the integrity and availability of the model or launch a free-riding attack. In this paper, we first combine the advantages of secret sharing, Diffie-Hellman key agreement, and functional encryption to develop an authenticable secure multi-party computing algorithm (SDF-ASMC). This algorithm can guarantee the security of transmitted data and provide authentication functionality in the absence of a trusted third party. Moreover, an efficient, secure, and authenticable FL algorithm (ESAFL), which leverages compressed sensing and all-or-nothing transform, is introduced to reduce the transmission and encryption of local gradients. Then, only the final element of the transformed measurements is encrypted by our proposed SDF-ASMC to protect all the measurements. This method effectively improves the efficiency of our algorithm. In addition, ESAFL also tolerates participants’ dropout. Security analysis demonstrates that our proposed algorithms can securely aggregate local gradients. Finally, the extensive experiments demonstrate the practical performance of our proposed algorithms.