An Efficient Greedy Hierarchical Federated Learning Training Method Based on Trusted Execution Environments

Abstract

With the continuous development of artificial intelligence, effectively solving the problem of data islands under the premise of protecting user data privacy has become a top priority. Federal learning is an effective solution to the two significant dilemmas of data islands and data privacy protection. However, there are still some security problems in federal learning. Therefore, this study simulates the data distribution in a hardware-based trusted execution environment in the real world through two processing methods: independent identically distributed and non-independent identically distributed methods. The basic model uses ResNet164 and innovatively introduces a greedy hierarchical training strategy to gradually train and aggregate complex models to ensure that the training of each layer is optimized under the premise of protecting privacy. The experimental results show that under the condition of an IID data distribution, the final accuracy of the greedy hierarchical model reaches 86.72%, which is close to the accuracy of the unpruned model at 89.60%. In contrast, under the non-IID condition, the model’s performance decreases. Overall, the TEE-based hierarchical federated learning method shows reasonable practicability and effectiveness in a resource-constrained environment. Through this study, the advantages of the greedy hierarchical federated learning model with regard to enhancing data privacy protection, optimizing resource utilization, and improving model training efficiency are further verified, providing new ideas and methods for solving the data island and data privacy protection problems.