Secure federated learning scheme based on adaptive Byzantine defense - ZHOU Yousheng; GAO Jingkun; ZUO Xiangjian; LIU Yuanni (Tongxin xuebao)
Aiming at the problem that the existing federated learning schemes cannot adaptively defend Byzantine attacks and low model accuracy, a secure federated learning scheme based on adaptive Byzantine defense was proposed. Through adaptive preliminary aggregation associated with incentives and global aggregation based on exponential weighted average, the global model was minimally perturbed on the premise of providing differential privacy perturbations for both the local model and the global model to achieve privacy protection. Different penalties were given to Byzantine client local models to adaptively defend Byzantine attacks, mobilized the enthusiasm of participants, and achieved higher model accuracy. Experimental results show that for different proportions of Byzantine clients, comparing the proposed scheme with other comparative schemes, the model accuracy is increased by 3.51%, 3.55% and 5.12% on average respectively, achieving higher model accuracy when adaptively defending Byzantine attacks.