Optimizing Federated Learning With Deep Reinforcement Learning for Digital Twin Empowered Industrial IoT

Yang, Wei, Xiang, Wei, Yang, Yuan, and Cheng, Peng (2023) Optimizing Federated Learning With Deep Reinforcement Learning for Digital Twin Empowered Industrial IoT. IEEE Transactions on Industrial Informatics, 19 (2). pp. 1884-1893.

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The accelerated development of the Industrial Internet of Things (IIoT) is catalyzing the digitalization of industrial production to achieve Industry 4.0. In this article, we propose a novel digital twin (DT) empowered IIoT (DTEI) architecture, in which DTs capture the properties of industrial devices for real-time processing and intelligent decision making. To alleviate data transmission burden and privacy leakage, we aim to optimize federated learning (FL) to construct the DTEI model. Specifically, to cope with the heterogeneity of IIoT devices, we develop the DTEI-assisted deep reinforcement learning method for the selection process of IIoT devices in FL, especially for selecting IIoT devices with high utility values. Furthermore, we propose an asynchronous FL scheme to address the discrete effects caused by heterogeneous IIoT devices. Experimental results show that our proposed scheme features faster convergence and higher training accuracy compared to the benchmark.

Item ID: 78439
Item Type: Article (Research - C1)
ISSN: 1941-0050
Keywords: Deep reinforcement learning (DRL), digital twin (DT), federated learning (FL), Industrial Internet of Things (IIoT), learning efficiency, real time
Copyright Information: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
Date Deposited: 10 Aug 2023 02:36
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4606 Distributed computing and systems software > 460603 Cyberphysical systems and internet of things @ 50%
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 50%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220402 Applied computing @ 100%
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