Cited by 316Privacy-Preserving Technologies in DataIoT and Edge/Fog ComputingDigital Transformation in Industry

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Communication-Efficient Federated Learning for Digital Twin Edge Networks in Industrial IoT

Lu, Yunlong, Huang, Xiaohong, Zhang, Ke, Maharjan, Sabita, Zhang, Yan·2021·IEEE Transactions on Industrial Informatics

DOI 10.1109/tii.2020.3010798

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Abstract

The rapid development of artificial intelligence and 5G paradigm, opens up new possibilities for emerging applications in industrial Internet of Things (IIoT). However, the large amount of data, the limited resources of Internet of Things devices, and the increasing concerns of data privacy, are major obstacles to improve the quality of services in IIoT. In this article, we propose the digital twin edge networks (DITENs) by incorporating digital twin into edge networks to fill the gap between physical systems and digital spaces. We further leverage the federated learning to construct digital twin models of IoT devices based on their running data. Moreover, to mitigate the communication overhead, we propose an asynchronous model update scheme and formulate the federated learning scheme as an optimization problem. We further decompose the problem and solve the subproblems based on the deep neural network model. Numerical results show that our proposed federated learning scheme for DITEN improves the communication efficiency and reduces the transmission energy cost.

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Authors: Lu, Y., Huang, X., Zhang, K., Maharjan, S., & Zhang, Y.
Affiliations: Beijing University of Posts and Telecommunications (CN)
University of Electronic Science and Technology of China (CN)
University of Oslo (NO)
ORCID: Lu, Yunlong
Huang, Xiaohong
Zhang, Ke
Maharjan, Sabita
Zhang, Yan
Venue: IEEE Transactions on Industrial Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2021-08
Volume: 17
Issue: 8
Pages: 5709-5718
ISSN: 1551-3203 (ISSN-L)
1941-0050
DOI: 10.1109/tii.2020.3010798
OpenAlex: W3044626808
Cited by: 316
References: 34
Citations by year: 2026: 36 · 2025: 57 · 2024: 65 · 2023: 67 · 2022: 56 · 2021: 31
FWCI: 15.28
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Industrial Informatics (publishedVersion)
Funders: National Natural Science Foundation of China (61941102, U1603261)
Opening Project of Shanghai Trusted Industrial Control Platform (TICPSH202003016-ZC)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
Keywords: federated learning, digital twin, edge networks, Industrial Internet of Things, communication efficiency, communication cost reduction, data privacy, deep neural network

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Communication-Efficient Federated Learning for Digital Twin Edge Networks in Industrial IoT

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