Abstract: Federated learning provides privacy protection for source data by exchanging model parameters or gradients.However,it still faces the problem of privacy disclosure.For example,membership inference attack can infer whether the target data samples are used to train machine learning models in federated learning.Aiming at the problem that the existing active membership inference attack based on model parameter construction in federated learning are less robust to dropout operations,an active membership inference attack method is proposed.This method makes use of the characteristic that the input of ReLU activation function is negative and the output is zero,constructs model parameters according to the target data,and inferences membership through the difference between member data and non-member data in updating model parameters.The redundancy of model neurons is used to construct multiple paths to achieve robustness to dropout.Experiments on MNIST,CIFAR10 and CIFAR100 datasets proves the effectiveness of our method.When dropout is used in model training,the proposed method can still achieve an accuracy of 100%.

Key words: Federated learning, Machine learning model, multiple redundant neurons, Active membership inference attack