A method for improving the robustness of federal learning systems based on input transformation

Federated learning is an effective method to solve the problem of data silos, but adversarial attacks launched based on adversarial samples pose a great threat to the security of federated learning models. This makes the application and promotion of federated learning somewhat affected. Therefore, this paper verifies the performance of a defense method for adversarial attacks in federated learning scenario, which is proposed in the traditional machine learning. The method defends adversarial attacks mainly by performing an input transformation before feeding images to the model. We conducted experiments on the EMnist dataset, and the experimental results show that this defense strategy can also improve the robustness of federation learning under different adversarial attacks.