Reinforcement Learning for Edge Device Selection using Social Attribute Perception in Industry 4.0

Abstract

In the 5G era, the problem of data islands in various industries restricts the development of artificial intelligence technology, so data sharing is proposed. High-quality data sharing directly affects the effectiveness of machine learning models, but data leakage and abuse will inevitably occur in the process. As a consequence, in order to solve this problem, federated learning is proposed. This method uses the personalized data of multiple edge devices to train the model. The central server collects the training results of the edge devices and updates the global model, and then iteratively tests and updates the model through the edge devices. However, edge devices may have problems such as unbalanced load and exit from the training process, which makes the training time of the model long and the effect is poor. Therefore, in the process of federated learning, the selection of reliable and high-quality edge devices becomes crucial. On this basis, in this paper, we introduces reinforcement learning (RL) to pre-select edge devices and obtain a set of candidate devices, then determines reliable edge devices through social attribute perception. Simulation experiment data analysis demonstrate that this scheme can improve the reliability of federated learning and complete the training process in a shorter time, the efficiency of federated learning increased by approximately 10.3%.