Federated learning is a kind of advanced distributed machine learning algorithm, which realizes multi-party cooperative training while ensuring the user's control over the data. However, the existing federated learning algorithms have many problems in dealing with Non-IID data, gradient information leakage and dynamic user offline. To solve these problems, this paper proposes a gradient hidden safe clustering and privacy-protecting federated learning based on quaternion, zero sharing and secret sharing techniques. Firstly, using quaternion rotation technology to hide the first-round model gradient and achieve secure clustering stratification without altering the gradient feature distribution, so as to solve the performance degradation issue caused by Non-IID data. Secondly, this paper designs a chain zero sharing algorithm, using single strategy to protect the user model gradient mask; Then, the threshold secret sharing is used to improve the robustness against offline users. Multi-dimensional comparison with other existing algorithms shows that the accuracy of SCFL is improved by about 3.13%-16.03% under the Non-IID data distribution, and the overall running time is improved by about 3-6 times. In this work, the security of information transmission is guaranteed at any stage, satisfying the design goals of accuracy, security and efficiency.

[2023-11-16] Accepted Paper /doc/2023.09.0403/v1