Image classification trusted model based on horizontal federated learning and SGX

Xinlin Wang, Shunxiang Lou, Haicheng Qu

Abstract: The rapid development of computer image processing has greatly improved the progress of artificial intelligence. When big data and distributed machine learning interact, the accuracy and efficiency of algorithm recognition can be greatly improved, but its security cannot be guaranteed in terms of privacy data protection. The traditional horizontal federated learning algorithm FedAvg can perform model training under the premise of protecting user data, but the security vulnerabilities in the parameter interaction process and the local data training process may be exploited by internal or external attackers. In order to balance the efficiency and security of the algorithm, this paper proposes an image classification model SGX-FedAvg. The model adds Intel SGX based on hardware encryption mechanism on the basis of horizontal federated learning FedAvg, which loses a small part of the algorithm efficiency of federated learning and adds reliable security to its model. In this paper, two data sets, Cifar-10 and Mnist, are selected for training. The model proposed in this paper is compared with Native local distributed machine learning and FedAvg horizontal federated learning. It is analyzed that SGX-FedAvg has good performance in execution efficiency and security within acceptable overhead, and its feasibility is verified. Finally, it points out which fields the model can be applied to.