基于自适应拜占庭防御的安全联邦学习方案OA北大核心CSTPCD

Aiming at the problem that the existing federated learning schemes cannot adaptively defend Byzantine at-tacks 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 perturba-tions for both the local model and the global model to achieve privacy protection.Different penalties were given to Byz-antine 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 pro-posed 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.