Edge AI for Industrial IoT Applications

Abstract

In this paper, we study the edge artificial intelligence (AI) for industrial internet of things (IIoT) applications. We discuss about the edge AI technology that is considered the combination of AI with edge computing and provide an overview of edge AI applications for IIoT networks, where the following three challenges are important to address: a) personalization, b) responsiveness and c) privacy preserving. To this end, we propose a federated active tranfer learning (FATL) model, which through training and testing is able to address those open challenges. Details about the training and testing of the proposed FATL global model are given including the corresponding simulation setup. This work concludes with a discussion and comparison of the obtained simulation results with existing edge AI training solutions, which provide useful insights about the proposed FATL model. The simulation results highlight how the FATL global model can address efficiently the open challenges of edge AI for future IIoT applications.