Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT

The continuous expanded scale of the industrial Internet of Things (IIoT) leads to IIoT equipments generating massive amounts of user data every moment. According to the different requirement of end users, these data usually have high heterogeneity and privacy, while most of users are reluctant to expose them to the public view. How to manage these time series data in an efficient and safe way in the field of IIoT is still an open issue, such that it has attracted extensive attention from academia and industry. As a new machine learning paradigm, federated learning (FL) has great advantages in training heterogeneous and private data. This article studies the FL technology applications to manage IIoT equipment data in wireless network environments. In order to increase the model aggregation rate and reduce communication costs, we apply deep reinforcement learning (DRL) to IIoT equipment selection process, specifically to select those IIoT equipment nodes with accurate models. Therefore, we propose a FL algorithm assisted by DRL, which can take into account the privacy and efficiency of data training of IIoT equipment. By analyzing the data characteristics of IIoT equipments, we use MNIST, fashion MNIST, and CIFAR-10 datasets to represent the data generated by IIoT. During the experiment, we employ the deep neural network model to train the data, and experimental results show that the accuracy can reach more than 97%, which corroborates the effectiveness of the proposed algorithm.

I. Introduction

Human society is rapidly moving towards the era of Industry 4.0 [1]. The global distribution of user equipments (UEs) is widespread and decentralized due to the influence of geographic location. Limited by the costs of transmission media (cables, optical fibers) and communication delays, traditional network infrastructures are not suitable for the development of Industry 4.0 [2]. On the other hand, wireless networks are widely used in Industry 4.0 because of its flexibility and portability. The industrial Internet of Things (IIoT) is one key technology to realize Industry 4.0. Many applications of IIoT are based on wireless networks, such as intelligent robots, driverless cars, smart grid, and smart medical [3]. Fig. 1 shows the IIoT scenario under the background of rapid development of social science and technology. A large number of IIoT equipments access to IIoT frequently, which can produce a huge amount of data in a short period of time [4], [5]. How to efficiently manage, store and use these time series data has become an important research topic.