Optimizing Federated Learning With Deep Reinforcement Learning for Digital Twin Empowered Industrial IoT
Journal Publication ResearchOnline@JCUAbstract
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.
Journal
IEEE Transactions on Industrial Informatics
Publication Name
N/A
Volume
19
ISBN/ISSN
1941-0050
Edition
N/A
Issue
2
Pages Count
10
Location
N/A
Publisher
Institute of Electrical and Electronics Engineers
Publisher Url
N/A
Publisher Location
N/A
Publish Date
N/A
Url
N/A
Date
N/A
EISSN
N/A
DOI
10.1109/TII.2022.3183465