基于自适应高斯混合的联邦学习后门防御方法OA

Aiming at the existing federated learning backdoor defense methods,which have the problems of misjudgment of abnormal client detection and are difficult to take into account the privacy protection of the client,we propose a federated learning backdoor defense approach based on adaptive Gaussian mixture model FedAGMM,which introduces Gaussian mixture model clustering at the server side,models the probability of gradient update of the client,and combines with the Bayesian information criterion to adaptively select the optimal number of clusters adaptively,so that the malicious model update is identified more accurately.Constructing a dynamic noise injection mechanism based on risk perception,adaptively adjusting the Gaussian noise intensity according to the client's risk level.This approach minimizes interference to normal clients while safeguarding privacy.Comparison experimental results with the latest defense methods show that in the face of three kinds of backdoor attacks,PGD,PGD-EDGE,and MR,the success rate of the attack is reduced by 5.80,3.27,and 1.00 percentage points,respectively,without decreasing the accuracy of the main task,and the theoretical analysis proves that FedAGMM meets the requirements of privacy protection while reducing overall noise injection,and significantly improves the detection accuracy and privacy security.