Industrial Iot Applications for AI Edge Computing Technology

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G. Selvapriya, G. Selvakumari


In this paper, we study the industrial iot applications for AI edge computing technology. 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 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. Here it concludes with a discussion and comparison of the simulation results with existing AI edge training solutions, where it provides useful insights about the proposed FALT model.

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