Federal learning-oriented sample poisoning attack resisting method

A federated learning-oriented confrontation sample poisoning attack method comprises the following steps: defining the following scenes: assuming that m participants participate in training, mgt; the k-th participant is assumed to be an attacker, and the attack target is that the performance of the federal learning global model on the test set is as poor as possible after the local model parameters participate in aggregation; the method comprises the steps that firstly, an attacker adds certain adversarial disturbance which cannot be perceived by human eyes to local private training samples to generate toxic adversarial samples, and local training is carried out based on the samples; secondly, in order to dominate the training process of the global model, an attacker improves the training learning rate in the local training process so as to accelerate the generation of malicious model parameters; and finally, an attacker uploads local model parameters to a server side to participate in aggregation so as to in