| At present,the security of artificial intelligence has aroused people’s general concern,prompting the implementation of a number of preventative measures.Federated learning mainly starts from the technical level,focusing on privacy and data security issues.On the one hand,most of the existing federated learning schemes adopt the encryption algorithm with the same key,making them vulnerable to collusion attacks by the malicious cloud server and participants.At the same time,attacks on model gradients can lead to private data leakage.On the other hand,resource heterogeneity and data quantity heterogeneity among the participants,resulting in their different contributions to the model.Reasonable assessment of participant performance and formulation of participant selection methods are also issues that should be paid attention to.In this paper,a data security sharing scheme in the federated learning scenario is proposed,which uses transmission deep learning model weights to resist collusion attacks among malicious servers and participants.Security analysis proves that the data inversed by malicious parties from model weights need to solve nonlinear equations,but this process is often difficult,secure data sharing is achieved.Considering the heterogeneity of different participants,such as different sizes and different distribution features,this paper designs a heterogeneous-aware adaptive participant selection method.The method instantly updates the stratification based on the observed training performance and accuracy to alleviate the impact of heterogeneity on model training time and accuracy.By adaptively selecting a subset of participants to participate in training,on the one hand,a large amount of communication cost caused by all participants participating in the training can be effectively reduced;on the other hand,abnormal participants can also be eliminated,and the risk of malicious attack is reduced.Based on MNIST and CIFAR10 data sets,this paper assesses the proposed scheme in terms of several performance indicators such as training time,model accuracy,and the amount of transmitted data.The results show that the proposed scheme meets the security requirements and improves both in the terms of the accuracy and time. |
