The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Communication-Efficient Federated Learning for Digital Twin Edge Networks in Industrial IoT
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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“Integration of an FL-based AI model can result in a more energy-efficient solution [177, 178].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [177]
Citations
Cites
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Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2873186 · Cited by 4,391
Industrial Internet of Things: Challenges, Opportunities, and Directions
Sisinni, E., Saifullah, A., Han, S., Jennehag, U., & Gidlund, M. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2852491 · Cited by 2,380
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
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Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
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Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT
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Self-citation
Research works printed but not linked: 2
- 2019, Convolutional Neural Netw
- glaessgen, 2020, The Digital Twin Paradigm for Future NASA and U S Air Force Vehicles[J]
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Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects
Mihai, S., Yaqoob, M., Hưng, Đ. V., Davis, W., Towakel, P., Raza, M., Karamanoglu, M., Barn, B. S., Shetve, D., Prasad, R. V., et al. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3208773 · Cited by 1,195
Digital Twin Networks: A Survey
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Self-citationFederated learning meets blockchain in edge computing: Opportunities and challenges
Nguyen, D. C., Ding, M., Pham, Q.-V., Pathirana, P. N., Le, L. B., Seneviratne, A., Li, J., Niyato, D., & Poor, H. V. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3072611 · Cited by 661
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Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023) · Cluster Computing · doi:10.1007/s10586-022-03658-4 · Cited by 399
Metadata
- 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)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2021-08
- Volume
- 17
- Issue
- 8
- Pages
- 5709-5718
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- 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
- 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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