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.
“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]
Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215 · see citing works
Advances and Open Problems in Federated Learning
Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772 · see citing works
A Survey on Mobile Edge Computing: The Communication Perspective
Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2745201 · Cited by 5,607 · see citing works
Digital Twin in Industry: State-of-the-Art
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 · see citing works
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 · see citing works
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Wang, S., Tuor, T., Salonidis, T., Leung, K. K., Makaya, C., He, T., & Chan, K. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2904348 · Cited by 2,232 · see citing works
Zhu, L., & Han, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-63076-8_2 · Cited by 1,906 · see citing works
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
Nishio, T., & Yonetani, R. (2019) · ICC 2019 - 2019 IEEE International Conference on Communications (ICC) · doi:10.1109/icc.2019.8761315 · Cited by 1,875 · see citing works
Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT
Lu, Y., Huang, X., Dai, Y., Maharjan, S., & Zhang, Y. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2942190 · Cited by 1,245 · see citing works
Self-citationFederated Learning over Wireless Networks: Optimization Model Design and Analysis
Tran, N. H., Bao, W., Zomaya, A. Y., Nguyen, M. N. H., & Hong, C. (2019) · IEEE Conference on Computer Communications · doi:10.1109/infocom.2019.8737464 · Cited by 943 · see citing works
Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning
Wang, Z., Song, M., Zhang, Z., Song, Y., Wang, Q., & Qi, H. (2019) · IEEE INFOCOM 2019 - IEEE Conference on Computer Communications · doi:10.1109/infocom.2019.8737416 · Cited by 881 · see citing works
Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks
Nasir, Y. S., & Guo, D. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2933973 · Cited by 660 · see citing works
Federated learning over wireless networks Convergence analysis and resource allocation
Dinh, C. T., Tran, N. H., Nguyen, M. N. H., Hong, C. S., Bao, W., Zomaya, A. Y., & Gramoli, V. (2021) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2020.3035770 · Cited by 483 · see citing works
Semisupervised Deep Reinforcement Learning in Support of IoT and Smart City Services
Mohammadi, M., Al‐Fuqaha, A., Guizani, M., & Oh, J.-S. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2712560 · Cited by 435 · see citing works
Hybrid deterministic-stochastic methods for data fitting
Friedlander, M. P., & Schmidt, M. (2011) · Cited by 314 · see citing works
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
Caldas, S., Konečny, J., McMahan, H. B., & Talwalkar, A. (2018) · arXiv · doi:10.48550/arxiv.1812.07210 · Cited by 312 · see citing works
Deep Learning Empowered Task Offloading for Mobile Edge Computing in Urban Informatics
Zhang, K., Zhu, Y., Leng, S., He, Y., Maharjan, S., & Zhang, Y. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2903191 · Cited by 309 · see citing works
Self-citationEdge Intelligence and Blockchain Empowered 5G Beyond for the Industrial Internet of Things
Zhang, K., Zhu, Y., Maharjan, S., & Zhang, Y. (2019) · IEEE Network · doi:10.1109/mnet.001.1800526 · Cited by 276 · see citing works
Self-citationDai, Y., Xu, D., Zhang, K., Maharjan, S., & Zhang, Y. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.2973705 · Cited by 262 · see citing works
Self-citationHybrid Deterministic-Stochastic Methods for Data Fitting
Friedlander, M. P., & Schmidt, M. (2012) · SIAM Journal on Scientific Computing · doi:10.1137/110830629 · Cited by 254 · see citing works
ATOMO: Communication-efficient Learning via Atomic SparsificationPreprint
Wang, H., Sievert, S., Charles, Z., Liu, S., Wright, S. J., & Papailiopoulos, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.04090 · Cited by 247 · see citing works
Optimal Joint Scheduling and Cloud Offloading for Mobile Applications
Mahmoodi, S. E., Uma, R. N., & Subbalakshmi, K. P. (2016) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2016.2560808 · Cited by 236 · see citing works
Federated Learning with Bayesian Differential Privacy
Triastcyn, A., & Faltings, B. V. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9005465 · Cited by 213 · see citing works
Bozorgchenani, A., Mashhadi, F., Tarchi, D., & Monroy, S. A. S. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.2994232 · Cited by 209 · see citing works
Computation Offloading in Multi-Access Edge Computing: A Multi-Task Learning Approach
Yang, B. F., Cao, X., Bassey, J., Li, X., & Qian, L. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.2990630 · Cited by 206 · see citing works
Dynamic Resource Allocation and Computation Offloading for IoT Fog Computing System
Chang, Z., Liu, L., Guo, X., & Sheng, Q. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2978946 · Cited by 174 · see citing works
Zhang, K., Leng, S., Peng, X., Pan, L., Maharjan, S., & Zhang, Y. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2872013 · Cited by 162 · see citing works
Self-citationA Novel Framework of Three-Hierarchical Offloading Optimization for MEC in Industrial IoT Networks
Zhao, Z., Zhao, R., Xia, J., Lei, X., Li, D., Yuen, C., & Fan, L. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2949348 · Cited by 156 · see citing works
Federated Learning Based Proactive Content Caching in Edge Computing
Yu, Z., Hu, J., Min, G., Lu, H., Zhao, Z., Wang, H., & Georgalas, N. (2018) · 2018 IEEE Global Communications Conference (GLOBECOM) · doi:10.1109/glocom.2018.8647616 · Cited by 140 · see citing works
Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach
Chen, X., Zhao, Z., Wu, C., Bennis, M., Liu, H., Ji, Y., & Zhang, H. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2933893 · Cited by 135 · see citing works
Incentive Mechanism for Edge-Computing-Based Blockchain
Chang, Z., Guo, W., Guo, X., Zhou, Z., & Ristaniemi, T. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2973248 · Cited by 67 · see citing works
Cluster Pruning: An Efficient Filter Pruning Method for Edge AI Vision Applications
Gamanayake, C., Jayasinghe, L., Ng, B. K. K., & Yuen, C. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2971418 · Cited by 58 · see citing works
A Digital Twin-Based Approach for Quality Control and Optimization of Complex Product Assembly
Ma, Y., Zhou, H., He, H., Jiao, G., & Wei, S. (2019) · International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) · doi:10.1109/aiam48774.2019.00157 · Cited by 40 · see citing works
Flagged first, then by citations
Cited by
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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 1,115 · see citing works
Digital Twin Networks: A Survey
Wu, Y., Zhang, K., & Zhang, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3079510 · Cited by 847 · see citing works
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 · see citing works
Xu, M., Ng, W. C., Lim, W. Y. B., Kang, J., Xiong, Z., Niyato, D. T., Yang, Q., Shen, X., & Miao, C. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3221119 · Cited by 588 · see citing works
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 · see citing works
Asynchronous federated learning on heterogeneous devices: A survey
Xu, C., Qu, Y., Xiang, Y., & Gao, L. (2023) · Computer Science Review · doi:10.1016/j.cosrev.2023.100595 · Cited by 366 · see citing works
Zhang, K., Cao, J., & Zhang, Y. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3088407 · Cited by 341 · see citing works
Self-citationA Survey on Digital Twin for Industrial Internet of Things: Applications, Technologies and Tools
Xu, H., Wu, J., Pan, Q., Guan, X., & Guizani, M. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3297395 · Cited by 304 · see citing works
Jagatheesaperumal, S. K., Rahouti, M., Ahmad, K., Al-Fuqaha, A. I., & Guizani, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3139827 · Cited by 199 · see citing works
Deep Reinforcement Learning for Stochastic Computation Offloading in Digital Twin Networks
Dai, Y., Zhang, K., Maharjan, S., & Zhang, Y. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3016320 · Cited by 298 · see citing works
Self-citationBlockchain and Federated Learning for Collaborative Intrusion Detection in Vehicular Edge Computing
Liu, H., Zhang, S., Zhang, P., Zhou, X., Shao, X., Pu, G., & Zhang, Y. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3076780 · Cited by 291 · see citing works
Self-citationAdaptive Edge Association for Wireless Digital Twin Networks in 6G
Lu, Y., Maharjan, S., & Zhang, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3098508 · Cited by 265 · see citing works
Self-citationAdvanced Manufacturing in Industry 5.0: A Survey of Key Enabling Technologies and Future Trends
Xiang, W., Yu, K., Han, F., Le, F., He, D., & Han, Q. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3274224 · Cited by 257 · see citing works
Machine Learning-Based Digital Twin for Predictive Modeling in Wind Turbines
Fahim, M., Sharma, V., Cao, T.-V., Canberk, B., & Duong, T. Q. (2022) · IEEE Access · doi:10.1109/access.2022.3147602 · Cited by 234 · see citing works
Space-Air-Ground Integrated Wireless Networks for 6G: Basics, Key Technologies, and Future Trends
Xiao, Y., Ye, Z., Wu, M., Li, H., Xiao, M., Alouini, M., Al‐Hourani, A., & Cioni, S. (2024) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2024.3492720 · Cited by 235 · see citing works
TFL-DT: A Trust Evaluation Scheme for Federated Learning in Digital Twin for Mobile Networks
Guo, J., Liu, Z., Tian, S., Huang, F., Li, J., Li, X., Kostromitin, K. I., & Ma, J. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3310094 · Cited by 223 · see citing works
Digital-Twin-Assisted Task Offloading Based on Edge Collaboration in the Digital Twin Edge Network
Liu, T., Tang, L., Wang, W., Chen, Q., & Zeng, X. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3086961 · Cited by 211 · see citing works
Yang, W., Xiang, W., Yang, Y. W., & Cheng, P. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3183465 · Cited by 192 · see citing works
Smart grid encounters edge computing: opportunities and applications
Feng, C., Wang, Y., Chen, Q., Ding, Y., Štrbac, G., & Kang, C. (2020) · Advances in Applied Energy · doi:10.1016/j.adapen.2020.100006 · Cited by 185 · see citing works
Digital Twin Assisted Task Offloading for Aerial Edge Computing and Networks
Li, B., Liu, Y., Tan, L., Pan, H., & Zhang, Y. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3182647 · Cited by 175 · see citing works
Self-citationDynamic Digital Twin and Federated Learning With Incentives for Air-Ground Networks
Sun, W. Y., Xu, N., Wang, L., Zhang, H., & Zhang, Y. (2020) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2020.3048137 · Cited by 174 · see citing works
Self-citationSun, W. Y., Wang, P., Xu, N., Wang, G., & Zhang, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3058213 · Cited by 173 · see citing works
Self-citationRoads Infrastructure Digital Twin: A Step Toward Smarter Cities Realization
Marai, O. E., Taleb, T., & Song, J. (2020) · IEEE Network · doi:10.1109/mnet.011.2000398 · Cited by 167 · see citing works
Survey on Digital Twin Edge Networks (DITEN) Toward 6G
Tang, F., Chen, X., Rodrigues, T. K., Zhao, M., & Kato, N. (2022) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2022.3197811 · Cited by 165 · see citing works
Digital Twin Empowered Content Caching in Social-Aware Vehicular Edge Networks
Zhang, K., Cao, J., Maharjan, S., & Zhang, Y. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3068369 · Cited by 152 · see citing works
Self-citationFederated learning in smart cities: Privacy and security survey
Al-Huthaifi, R., Li, T., Huang, W. X., Gu, J., & Li, C. (2023) · Information Sciences · doi:10.1016/j.ins.2023.03.033 · Cited by 160 · see citing works
BAFL: A Blockchain-Based Asynchronous Federated Learning Framework
Feng, L., Zhao, Y., Guo, S., Qiu, X., Li, W., & Yu, P. (2021) · IEEE Transactions on Computers · doi:10.1109/tc.2021.3072033 · Cited by 150 · see citing works
UAV-Enabled Covert Federated Learning
Hou, X., Wang, J., Jiang, C., Zhang, X., Ren, Y., & Debbah, M. A. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2023.3245621 · Cited by 141 · see citing works
Application-Driven Network-Aware Digital Twin Management in Industrial Edge Environments
Bellavista, P., Giannelli, C., Mamei, M., Mendula, M., & Picone, M. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3067447 · Cited by 141 · see citing works
Zeb, S., Mahmood, A., Hassan, S. A., Piran, M. J., Gidlund, M., & Guizani, M. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2021.103309 · Cited by 132 · see citing works
Guo, Q., Tang, F., & Kato, N. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3227655 · Cited by 128 · see citing works
Digital Twin for 6G: Taxonomy, Research Challenges, and the Road Ahead
Masaracchia, A., Sharma, V., Canberk, B., Dobre, O. A., & Duong, T. Q. (2022) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2022.3219015 · Cited by 129 · see citing works
A Survey on Resource Management in Joint Communication and Computing-Embedded SAGIN
Chen, Q., Guo, Z., Meng, W., Han, S., Li, C., & Quek, T. Q. S. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3421523 · Cited by 129 · see citing works
BlockNet: Beyond reliable spatial Digital Twins to Parallel Metaverse
Lv, Z., Qiao, L., Li, Y., Yuan, Y., & Wang, F. (2022) · Patterns · doi:10.1016/j.patter.2022.100468 · Cited by 118 · see citing works
A review of digital twin capabilities, technologies, and applications based on the maturity model
Liu, Y., Feng, J., Lu, J., & Zhou, S. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102592 · Cited by 123 · see citing works
Digital Twin-Driven Vehicular Task Offloading and IRS Configuration in the Internet of Vehicles
Yuan, X., Chen, J., Zhang, N., Ni, J., Yu, F. R., & Leung, V. C. M. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3204585 · Cited by 110 · see citing works
RADiT: Resource Allocation in Digital Twin-Driven UAV-Aided Internet of Vehicle Networks
Hazarika, B., Singh, K., Li, C.-P., Schmeink, A., & Tsang, K. F. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3310048 · Cited by 109 · see citing works
Jiang, L., Zheng, H., Tian, H., Xie, S., & Zhang, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3126207 · Cited by 104 · see citing works
Self-citationZhao, T.-T., Li, F., & He, L.-J. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3170900 · Cited by 94 · see citing works
Ji, Z., Wu, S. Y., & Jiang, C. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3313595 · Cited by 104 · see citing works
An update method for digital twin multi-dimension models
Zhang, H., Qi, Q., Ji, W., & Tao, F. (2022) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102481 · Cited by 103 · see citing works
Wu, H., Ji, P., Ma, H., & Xing, L. (2023) · Sensors · doi:10.3390/s23198306 · Cited by 119 · see citing works
Sheraz, M., Chuah, T. C., Lee, Y. L., Alam, M. M., Al-Habashna, A., & Han, Z. (2024) · IEEE Access · doi:10.1109/access.2024.3384272 · Cited by 106 · see citing works
Trusted AI in Multiagent Systems: An Overview of Privacy and Security for Distributed Learning
Ma, C., Li, J., Wei, K., Liu, B., Ding, M., Yuan, L., Han, Z., & Vincent Poor, H. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3306773 · Cited by 96 · see citing works
Lin, X., Wu, J., Li, J., Yang, W., & Guizani, M. (2021) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3122013 · Cited by 92 · see citing works
Byzantine-Robust Aggregation in Federated Learning Empowered Industrial IoT
Li, S., Ngai, E. C. ‐., & Voigt, T. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3128164 · Cited by 89 · see citing works
Digital Twin-Assisted Edge Computation Offloading in Industrial Internet of Things With NOMA
Zhang, L., Wang, H., Xue, H., Zhang, H., Liu, Q., Niyato, D. T., & Han, Z. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3270859 · Cited by 81 · see citing works
Federated Learning in Robotic and Autonomous Systems
Xianjia, Y., Queralta, J. P., Heikkonen, J., & Westerlund, T. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.07.041 · Cited by 79 · see citing works
Digital twin intelligent transportation system (DT‐ITS)—A systematic review
Ge, C., & Qin, S. (2024) · IET Intelligent Transport Systems · doi:10.1049/itr2.12539 · Cited by 83 · see citing works
Intrusion Detection for Maritime Transportation Systems With Batch Federated Aggregation
Liu, W., Xu, X., Wu, L., Qi, L., Jolfaei, A., Ding, W., & Khosravi, M. R. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3181436 · Cited by 73 · see citing works
Federated Reinforcement Learning-Based Resource Allocation in D2D-Enabled 6G
Guo, Q., Tang, F., & Kato, N. (2022) · IEEE Network · doi:10.1109/mnet.122.2200102 · Cited by 72 · see citing works
Mengistu, T. M., Kim, T., & Lin, J.-W. (2024) · Sensors · doi:10.3390/s24030968 · Cited by 79 · see citing works
Wireless Federated Learning (WFL) for 6G Networks⁴Part I: Research Challenges and Future Trends
Bouzinis, P. S., Diamantoulakis, P. D., & Karagiannidis, G. K. (2021) · IEEE Communications Letters · doi:10.1109/lcomm.2021.3121071 · Cited by 67 · see citing works
A Greedy Agglomerative Framework for Clustered Federated Learning
Mehta, M., & Shao, C. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3252599 · Cited by 67 · see citing works
Jameil, A. K., & Al-Raweshidy, H. S. (2025) · Discover Internet of Things · doi:10.1007/s43926-025-00135-3 · Cited by 81 · see citing works
A review of the technology standards for enabling digital twin
Wang, K., Wang, Y., Li, Y., Fan, X., Xiao, S., & Hu, L. (2022) · Digital Twin · doi:10.12688/digitaltwin.17549.1 · Cited by 101 · see citing works
Digital Twin Models: Functions, Challenges, and Industry Applications
Rayhana, R., Bai, L., Xiao, G. G., Liao, M., & Liu, Z. (2024) · IEEE Journal of Radio Frequency Identification · doi:10.1109/jrfid.2024.3387996 · Cited by 68 · see citing works
Federated Learning: Navigating the Landscape of Collaborative Intelligence
Lazaros, K., Koumadorakis, D. E., Vrahatis, A. G., & Kotsiantis, S. B. (2024) · Electronics · doi:10.3390/electronics13234744 · Cited by 66 · see citing works
Digital Twins From a Networking Perspective
Vaezi, M., Noroozi, K., Todd, T. D., Zhao, D., Karakostas, G., Wu, H., & Shen, X. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3200327 · Cited by 65 · see citing works
Communication-efficient distributed AI strategies for the IoT edge
Mwase, C., Jin, Y., Westerlund, T., Tenhunen, H., & Zou, Z. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.01.013 · Cited by 64 · see citing works
Deep Federated Learning for IoT-based Decentralized Healthcare Systems
Elayan, H., Aloqaily, M., & Guizani, M. (2021) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc51323.2021.9498820 · Cited by 61 · see citing works
Digital Twin Empowered Heterogeneous Network Selection in Vehicular Networks With Knowledge Transfer
Zheng, J., Luan, T. H., Hui, Y., Yin, Z., Cheng, N., Gao, L., & Cai, L. X. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3190941 · Cited by 59 · see citing works
GRU-based digital twin framework for data allocation and storage in IoT-enabled smart home networks
Singh, S. K., Kumar, M., Tanwar, S., & Park, J. H. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.12.009 · Cited by 57 · see citing works
B5GEMINI: AI-Driven Network Digital Twin
Mozó, A., Karamchandani, A., Gómez-Canaval, S., Sanz, M., Moreno, J. I., & Pastor, A. (2022) · Sensors · doi:10.3390/s22114106 · Cited by 53 · see citing works
Jiang, L., Liu, Y., Tian, H., Tang, L., & Xie, S. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3357827 · Cited by 55 · see citing works
Network Digital Twin for 6G and Beyond: An End-to-End View Across Multi-Domain Network Ecosystems
Tran, D.-H., Waheed, N., Saputra, Y. M., Lin, X., Nguyen, C. T., Abdu, T. S., Vo, V. N., Pham, V., Alsenwi, M., Adam, A. B. M., et al. (2025) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2025.3599866 · Cited by 61 · see citing works
Moniruzzaman, M., Yassine, A., & Benlamri, R. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3262597 · Cited by 51 · see citing works
Adaptive Federated Learning for Digital Twin Driven Industrial Internet of Things
Song, Q., Lei, S., Sun, W. Y., & Zhang, Y. (2021) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc49053.2021.9417370 · Cited by 51 · see citing works
Self-citationJoint resource management for mobility supported federated learning in Internet of Vehicles
Wang, G., Xu, F., Zhang, H., & Zhao, C. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.11.020 · Cited by 50 · see citing works
Integration of Sensing, Communication, and Computing for Metaverse: A Survey
Wang, X., Guo, Q., Ning, Z., Guo, L., Wang, G., Gao, X., & Zhang, Y. (2024) · ACM Computing Surveys · doi:10.1145/3659946 · Cited by 50 · see citing works
Self-citationHe, Y., Yang, M., He, Z., & Guizani, M. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3205778 · Cited by 49 · see citing works
Hao, Y., Wang, J., Huo, D., Guizani, N., Hu, L., & Chen, M. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3310051 · Cited by 48 · see citing works
A systematic review of federated learning from clients’ perspective: challenges and solutions
Shanmugarasa, Y., Paik, H.-Y., Kanhere, S. S., & Zhu, L. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10563-8 · Cited by 52 · see citing works
GNN-Based Power Allocation and User Association in Digital Twin Network for the Terahertz Band
Zhang, H., Ma, X., Liu, X., Li, L., & Sun, K. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3313192 · Cited by 47 · see citing works
DT-Assisted Multi-Point Symbiotic Security in Space-Air-Ground Integrated Networks
Yin, Z., Cheng, N., Luan, T. H., Song, Y., & Wang, W. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3313326 · Cited by 46 · see citing works
Hazarika, B., Singh, K., Paul, A., & Duong, T. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3335277 · Cited by 37 · see citing works
Federated learning at the edge in Industrial Internet of Things: A review
Kumar, S., Vahabi, M., & Fotouhi, H. (2025) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2025.101087 · Cited by 59 · see citing works
Resource optimizing federated learning for use with IoT: A systematic review
Ferreira, L., Sadok, D. F. H., & Endo, P. T. (2023) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2023.01.006 · Cited by 45 · see citing works
Management of Digital Twin-Driven IoT Using Federated Learning
AbdulRahman, S., Otoum, S., Bouachir, O., & Mourad, A. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3310102 · Cited by 45 · see citing works
A Survey of Multi-Access Edge Computing and Vehicular Networking
Hou, L., Gregory, M. A., & Li, S. (2022) · IEEE Access · doi:10.1109/access.2022.3224032 · Cited by 46 · see citing works
Nguyen, M.-D., Lee, S., Pham, Q., Hoang, D. T., Nguyen, D. N., & Hwang, W. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3190510 · Cited by 43 · see citing works
Radar Imaging Based UAV Digital Twin for Wireless Channel Modeling in Mobile Networks
Xie, W., Qi, F., Liu, L., & Liu, Q. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3310085 · Cited by 42 · see citing works
Hong, Y., Wu, J., & Morello, R. (2024) · Scientific Reports · doi:10.1038/s41598-024-69474-5 · Cited by 42 · see citing works
Security and privacy of industrial big data: Motivation, opportunities, and challenges
Anjum, N., Latif, Z., & Chen, H. (2025) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2025.104130 · Cited by 41 · see citing works
Incentive-Based Federated Learning for Digital-Twin-Driven Industrial Mobile Crowdsensing
Li, B., Shi, Y., Kong, Q., Du, Q., & Lu, R. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3279657 · Cited by 41 · see citing works
Computation Offloading and Resource Allocation Based on DT-MEC-Assisted Federated Learning Framework
He, Y., Yang, M., He, Z., & Guizani, M. (2023) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2023.3298926 · Cited by 39 · see citing works
Network Digital Twin Toward Networking, Telecommunications, and Traffic Engineering: A Survey
Poorzare, R., Kanellopoulos, D. N., Sharma, V. K., Dalapati, P., & Waldhorst, O. P. (2025) · IEEE Access · doi:10.1109/access.2025.3531947 · Cited by 40 · see citing works
Incentive Mechanism for Differentially Private Federated Learning in Industrial Internet of Things
Xu, Y., Xiao, M., Tan, H., Liu, A., Gao, G., & Yan, Z. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3134257 · Cited by 39 · see citing works
Tran-Dang, H., & Kim, D.-S. (2025) · ICT Express · doi:10.1016/j.icte.2025.01.002 · Cited by 38 · see citing works
A review of the technology standards for enabling digital twin
Wang, K., Wang, Y., Li, Y., Fan, X., Xiao, S., & Hu, L. (2022) · Digital Twin · doi:10.12688/digitaltwin.17549.2 · Cited by 38 · see citing works
Secure Task Offloading in Blockchain-Enabled MEC Networks With Improved PBFT Consensus
Du, J., Yu, Z., Sun, A., Jiang, J., Zhao, H., Zhang, N., Wu, C., & Yu, F. R. (2024) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2024.3454280 · Cited by 38 · see citing works
Energy-Efficient Task Transfer in Wireless Computing Power Networks
Lu, Y., Ai, B., Zhong, Z. D., & Zhang, Y. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3223690 · Cited by 37 · see citing works
Self-citationMahbub, M., & Shubair, R. M. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103726 · Cited by 42 · see citing works
Bi-Directional Digital Twin and Edge Computing in the Metaverse
Yu, J., Alhilal, A. Y., Hui, P., & Tsang, D. H. K. (2024) · IEEE Internet of Things Magazine · doi:10.1109/iotm.001.2300173 · Cited by 37 · see citing works
Digital Twin-Assisted Federated Learning Service Provisioning Over Mobile Edge Networks
Zhang, R., Xie, Z., Yu, D., Liang, W., & Cheng, X. (2023) · IEEE Transactions on Computers · doi:10.1109/tc.2023.3337317 · Cited by 36 · see citing works
Resources-Efficient Adaptive Federated Learning for Digital Twin-Enabled IIoT
Qiao, D., Li, M., Guo, S., Zhao, J., & Xiao, B. (2024) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2024.3382206 · Cited by 36 · see citing works
Internet of Federated Digital Twins: Connecting Twins Beyond Borders for Society 5.0
Yu, T., Li, Z., Hashash, O., Sakaguchi, K., Saad, W., & Debbah, M. A. (2024) · IEEE Internet of Things Magazine · doi:10.1109/iotm.001.2300279 · Cited by 36 · see citing works
Digital Twin Enabled Asynchronous SplitFed Learning in E-Healthcare Systems
Stephanie, V., Khalil, I., & Atiquzzaman, M. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3310103 · Cited by 35 · see citing works
Edge Intelligence for Industrial IoT: Opportunities and Limitations
Savaglio, C., Mazzei, P., & Fortino, G. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.01.039 · Cited by 34 · see citing works
Islam, A., & Shin, S. Y. (2023) · IEEE Network · doi:10.1109/mnet.001.2200484 · Cited by 33 · see citing works
- 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 · 2020: 4
- 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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