Enhanced Privacy-Preserving Federated Learning with Exit Tolerance and Verifiable Aggregation

Abstract

With the escalating importance of data privacy, Federated Learning (FL) has drawn significant attention as a machine learning framework that facilitates collaborative model training among multiple users without the need for direct data sharing. This paper presents an enhanced Federated Learning scheme designed to bolster user privacy protection while maintaining the efficiency and performance of model training. Our approach integrates the use of random seeds and One-Time Password (OTP) technology to reinforce data encryption, and employs an advanced masking mechanism coupled with bilinear pairing for verification, thereby enhancing the security of the aggregation process. Additionally, our design accommodates user exits during the training process without compromising the overall training outcome. Through rigorous experimental analysis, we have demonstrated the effectiveness of our scheme, showcasing its acceptable computational and communication overheads. This research introduces a novel solution to privacy preservation challenges in Federated Learning, laying a solid foundation for the advancement of related technologies.