Verifiable Secure Aggregation Protocol Under Federated Learning

Abstract

Federated learning is a new machine learning paradigm used for collaborative training models among multiple devices. In federated learning, multiple clients participate in model training locally and use decentralized learning methods to ensure the privacy of client data. However, although federated learning protects the privacy of client data, the update gradients uploaded by clients may still contain sensitive information. To solve this problem, this paper proposes a secure aggregation protocol which can verify the aggregation results under federated learning and protect gradient privacy. The core idea of this aggregation protocol is to use encryption technology to achieve secure computation between clients, ensuring the privacy of gradients during the aggregation process. At the same time, bilinear pairing technology is used to achieve the verifiability of aggregation results, ensuring the correctness and usability of the model after aggregation. In order to evaluate the security of the protocol, this paper conducts a detailed security analysis. The results show that this protocol has higher security properties compared to the existing related protocols. In addition, the computation and communication costs of the protocol are analyzed, which show that the protocol has good credibility and applicability in practical federated learning scenarios.