Abstract: As a distributed machine learning,federated learning alleviates the problem of data islands,which only transmits model parameters between the server and the client without sharing local data and improves the privacy of training data,at the same time it also makes federated learning vulnerable to malicious client attacks.The existing research mainly focuses on intercepting updates uploaded by malicious clients.A federated learning model based on update quality detection and malicious client identification method,named umFL,is studied to improve the training performance of global models and the robustness of federated learning.Specifically,the client importance is calculated by obtaining the loss value of each round of client training.The subset of clients participating in each round of training is selected by update quality detection.The similarity between the updated local model and the previous round of global model is calculated to determine whether the client makes positive updates and the negative updates are filtered.Meanwhile,the beta distribution function is introduced to update the client reputation value.The clients with low reputation value are marked as malicious clients and excluded from participating in subsequent training.The effectiveness of the proposed algorithm on MNIST and CIFAR10 datasets is tested by using convolutional neural networks respectively.Experimental results show that under the attack of 20%~40% of malicious clients,the proposed model is still safe.Especially under the 40% malicious clients,the umFL model improves the model testing accuracy by 40% and 20% on MNIST and CIFAR10 respectively compared with traditional federated learning,and the model convergence speed is also improved by 25.6% and 22.8% respectively.

Key words: Federated learning, Client update quality, Client reputation value, Malicious user indentification, Client selection