Edge Artificial Intelligence for Industrial Internet of Things Applications: An Industrial Edge Intelligence Solution
Fotis Foukalas, Athanasios Tziouvaras
Abstract
In this article, we study edge artificial intelligence (AI) for industrial Internet of Things (IIoT) applications. We discuss edge AI technology, which 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: 1) personalization, 2) responsiveness, and 3) privacy preservation. To this end, we propose a federated active transfer 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 efficiently address the open challenges of edge AI for future IIoT applications.