Abstract: The federated learning method breaks the bottleneck of data barriers and has been widely used in finacial and medical aided diagnosis.However,the existence of counter attacks poses a potential threat to the security of federated learning models.In order to ensure the safety of the federated learning model in actual scenarios,improving the robustness of the model is a good solution.Adversarial training is a common method to enhance the robustness of the model,but the traditional adversarial training methods are all aimed at centralized machine learning,and usually can only defend against specific adversarial attacks.This paper proposes an adversarial training method to enhance the robustness of the model in a federated learning scenario.This method adds single-strength adversarial samples,iterative adversarial samplesand normal samples in the training process,and adjusts the weight of the loss function under each training sample to complete the local training and the update of global model.Experiments on Mnist and Fashion_Mnist datasetsshow that although the adversarial training is only based on FGSM and PGD,this adversa-rial training method greatly enhances the robustness of the federated model in different attack scenarios.The adversarial training is based on FGSM and PGD,and is also effective for other attck methods such as FFGSM,PGDDLR,BIM and so on.

Key words: Adversarial attack, Adversarial training, Federated learning, Robustness