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

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

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

DOI 10.1109/tii.2020.3010798

Open this paper in CoChat — free account

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.

Integrity signals

Registry

Crossref · 10.1109/tii.2020.3010798

Cites

34 of 34 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

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
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
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
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
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
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
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
Deep Leakage from Gradients
Zhu, L., & Han, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-63076-8_2 · Cited by 1,906
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
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
Self-citation

Show all 34 references

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]

Sorted by citations

Items 101–200 of 315

DSAC-Configured Differential Evolution for Cloud–Edge–Device Collaborative Task Scheduling
Laili, Y., Wang, X., Li, Z., & Ren, L. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3281661 · Cited by 33
Data Augmentation for Predictive Digital Twin Channel: Learning Multi-Domain Correlations by Convolutional TimeGAN
Liang, G., Hu, J., Yang, K., Song, S., Liu, T., Xie, N., & Yu, Y. (2024) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2024.3358980 · Cited by 33
Over-the-Air Federated Learning in Digital Twins Empowered UAV Swarms
Jiang, B., Du, J., Jiang, C., Han, Z., Alhammadi, A., & Debbah, M. A. (2024) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2024.3455009 · Cited by 35
Pavement Crack Detection Based on 3D Edge Representation and Data Communication With Digital Twins
Cao, T., Wang, Y., & Liu, S. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3194013 · Cited by 33
Edge computing-based digital twin framework based on ISO 23247 for enhancing data processing capabilities
Kang, M.-S., Lee, D.-H., Bajestani, M. S., Kim, D. B., & Noh, S. D. (2024) · Machines · doi:10.3390/machines13010019 · Cited by 32
A Survey on Integrated Sensing, Communication, and Computing Networks for Smart Oceans
Dai, M., Li, Y., Li, P., Wu, Y., Qian, L., Lin, B., & Su, Z. (2022) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan11040070 · Cited by 36
Selecting Privacy-Enhancing Technologies for Managing Health Data Use
Jordan, S. R., Fontaine, C., & Hendricks‐Sturrup, R. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.814163 · Cited by 35
Digital Twin Assisted Risk-Aware Sleep Mode Management Using Deep Q-Networks
Masoudi, M., Soroush, E., Zander, J., & Çavdar, Ç. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3206498 · Cited by 39
Urban flooding digital twin system framework
Ge, C., & Qin, S. (2025) · Systems Science & Control Engineering · doi:10.1080/21642583.2025.2460432 · Cited by 35
A multi-level modelling and fidelity evaluation method of digital twins for creating smart production equipment in Industry 4.0
Zhang, C., Li, J., Zhou, G., Huang, Q., Zhang, M., Zhi, Y., & Wei, Z. (2023) · International Journal of Production Research · doi:10.1080/00207543.2023.2246161 · Cited by 29
A secure and flexible edge computing scheme for AI-driven industrial IoT
Zhao, Y. L., Hu, N., Zhao, Y., & Zhu, Z. (2021) · Cluster Computing · doi:10.1007/s10586-021-03400-6 · Cited by 28
Boosting Accuracy of Differentially Private Federated Learning in Industrial IoT With Sparse Responses
Cui, L., Ma, J., Zhou, Y., & Yu, S. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3161517 · Cited by 28
Cloud-Edge-Client Collaborative Learning in Digital Twin Empowered Mobile Networks
Zhao, L., Ni, S., Wu, D., & Zhou, L. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3310060 · Cited by 29
Navigating the Digital Twin Network landscape: A survey on architecture, applications, privacy and security
Sai, A. M. V. V., Wang, C., Cai, Z., & Li, Y. (2024) · High-Confidence Computing · doi:10.1016/j.hcc.2024.100269 · Cited by 30
Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach
Mishra, K. N., Bhattacharjee, V., Saket, S., & Mishra, S. P. (2022) · Cluster Computing · doi:10.1007/s10586-022-03813-x · Cited by 25
UAV Base Station Network Transmission-Based Reverse Auction Mechanism for Digital Twin Utility Maximization
Zhang, J., Zong, M., Vasilakos, A. V., & Li, W. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3301522 · Cited by 24
EDT-SaFL: Semi-Asynchronous Federated Learning for Edge Digital Twin in Industrial Internet-of-Things
Tao, M., Liao, L., Zhang⋆, Y., Liu, L., Min, G., Niyato, D. T., & Dustdar, S. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3595117 · Cited by 24
Self-citation
Digital twin oriented architecture for secure and QoS aware intelligent communications in industrial environments
Bellavista, P., Giannelli, C., Mamei, M., Mendula, M., & Picone, M. (2022) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2022.101646 · Cited by 24
Digital Twin Empowered Industrial IoT Based on Credibility-Weighted Swarm Learning
Xiang, W., Li, J., Zhou, Y., Cheng, P., Jin, J., & Yu, K. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3264289 · Cited by 25
Distributed Resource Optimization With Blockchain Security for Immersive Digital Twin in IIoT
Song, J., Kang, Y., Song, Q., Guo, L., & Jamalipour, A. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3224986 · Cited by 25
A Novel Design Method of High Throughput Blockchain for 6G Networks: Performance Analysis and Optimization Model
Ni, Q., Zhang, L., Zhu, X., & Ali, I. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3194889 · Cited by 22
Joint Design of Communication and Computing for Digital-Twin-Enabled Aircraft Final Assembly
Ren, C., Chen, C., Wen, X., Ma, Y., Zhu, S., & Guan, X. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3266278 · Cited by 21
Federated Learning in Industrial IoT: A Privacy-Preserving Solution That Enables Sharing of Data in Hydrocarbon Explorations
Hu, X., Cai, H., Alazab, M., Zhou, W., Haghighi, M. S., & Wen, S. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3306931 · Cited by 21
Efficient Privacy‐Preserving Federated Deep Learning for Network Intrusion of Industrial IoT
He, N., Zhang, Z., Wang, X., & Gao, T. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/2956990 · Cited by 25
Digital Twin
Zhang, Y. (2024) · Simula SpringerBriefs on computing · doi:10.1007/978-3-031-51819-5 · Cited by 25
Self-citation
Joint Vehicle Connection and Beamforming Optimiziation in Digital-Twin-Assisted Integrated Sensing and Communication Vehicular Networks
Ding, W., Yang, Z., Chen, M., Liu, Y., & Shikh‐Bahaei, M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3420774 · Cited by 21
A Comprehensive Survey on Digital Twin: Focusing on Security Threats and Requirements
Mun, H., Han, K., Damiani, E., Yeun, H. K., Kim, T., Martino, L., & Yeun, C. Y. (2025) · IEEE Access · doi:10.1109/access.2025.3563621 · Cited by 26
Decentralized Federated Learning With Markov Chain Based Consensus for Industrial IoT Networks
Du, M., Zheng, H., Feng, X., Chen, Y., & Zhao, T. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3192297 · Cited by 20
Digital-Twin-Assisted Edge-Computing Resource Allocation Based on the Whale Optimization Algorithm
Qiu, S., Zhao, J., Lv, Y., Dai, J., Chen, F., Wang, Y., & Li, A. (2022) · Sensors · doi:10.3390/s22239546 · Cited by 22
Lightweight Industrial Image Classifier Based on Federated Few-Shot Learning
Sun, X., Yang, S., & Zhao, C. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3210600 · Cited by 21
Blockchain-Empowered Privacy-Preserving Digital Object Trading in the Metaverse
Xiao, Y., Xu, L., Zhang, C., Zhu, L., & Zhang, Y. (2023) · IEEE Multimedia · doi:10.1109/mmul.2023.3246528 · Cited by 20
Self-citation
Internet of Digital Twin: Framework, Applications, and Enabling Technologies
Liu, R., Luan, T. H., Qu, Y., Xiang, Y., Gao, L., & Zhao, D. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3554579 · Cited by 21
Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study
Alotaibi, B. K., Khan, F. A., & Mahmood, S. (2024) · Applied Sciences · doi:10.3390/app14072720 · Cited by 23
Research on Wireless Sensor Network Access Control and Load Balancing in the Industrial Digital Twin Scenario
Zhou, W. H. (2022) · Journal of Sensors · doi:10.1155/2022/3929958 · Cited by 22
Communication-Efficient Personalized Federated Learning With Privacy-Preserving
Wang, Q., Chen, S., & Wu, M. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3323129 · Cited by 19
Sustainable Resource Allocation and Reduce Latency Based on Federated-Learning-Enabled Digital Twin in IoT Devices
Alhartomi, M. A., Salh, A., Audah‏, L., Alzahrani, S., Alzahmi, A., Altimania, M. R., Alotaibi, A., Alsulami, R., & Al‐Hartomy, O. A. (2023) · Sensors · doi:10.3390/s23167262 · Cited by 18
A Self-Healing and Fault-Tolerant Cloud-Based Digital Twin Processing Management Model
Saxena, D., & Singh, A. K. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3540498 · Cited by 17
QoE-Driven Wireless Communication Resource Allocation Based on Digital Twin Edge Network
Zhao, J., Chen, Y., & Huang, Y. X. (2023) · IEEE Journal of Radio Frequency Identification · doi:10.1109/jrfid.2023.3317184 · Cited by 16
Reduction in Data Imbalance for Client-Side Training in Federated Learning for the Prediction of Stock Market Prices
Shaheen, M., Farooq, M. S., & Umer, T. (2023) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan13010001 · Cited by 15
Communication-Efficient Federated Learning for Digital Twin Systems of Industrial Internet of Things
Zhao, Y., Li, L., Liu, Y., Fan, Y., & Lin, K. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.04.232 · Cited by 17
Blockchain and FL-based Network Resource Management for Interactive Immersive Services
Aloqaily, M., Bouachir, O., & Ridhawi, I. A. (2021) · Globecom · doi:10.1109/globecom46510.2021.9685091 · Cited by 14
Blockchain‐based multi‐layered federated extreme learning networks in connected vehicles
Durga, R., Poovammal, E., Srivastava, G., Ramana, K., & Iwendi, C. (2022) · Expert Systems · doi:10.1111/exsy.13222 · Cited by 18
A spatiotemporal intelligent framework and experimental platform for urban digital twins
Hu, J., Lv, Z., Yuan, D., He, B., Chen, W., Ye, X., Li, D., & Yang, G. (2023) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2022.08.018 · Cited by 16
Hypergraph-Based Resource-Efficient Collaborative Reinforcement Learning for B5G Massive IoT
Yang, F., Yang, C., Jie, H., Yu, K., Garg, S., & Alrashoud, M. R. (2023) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2023.3321310 · Cited by 14
Optimized Task Scheduling and Virtual Object Management Based on Digital Twin for Distributed Edge Computing Networks
Xu, R., Park, C., Khan, S., Jin, W., Moe, S. J. S., & Kim, D. H. (2023) · IEEE Access · doi:10.1109/access.2023.3325475 · Cited by 14
Living on the edge: A survey of Digital Twin-Assisted Task Offloading in safety-critical environments
do Carmo, P. R. X., Bezerra, D. D. F., Filho, A. T. D. O., Freitas, E., Silva, M. L. P. C., Dantas, M., Oliveira, B., Kelner, J., Sadok, D. F. H., & Souza, R. (2024) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2024.104024 · Cited by 14
Artificial Intelligence-Aided Digital Twin Design: A Systematic ReviewPreprint
Hao, N., Li, Y., Liu, K., Liu, S., Lu, Y., Xu, B., Li, C., Chen, J., Yue, L., Fu, T., et al. (2024) · Preprints.org · doi:10.20944/preprints202408.2063.v1 · Cited by 16
Self-citation
A Label Efficient Semi Self-Supervised Learning Framework for IoT Devices in Industrial Process
Bharti, V., Kumar, A., Purohit, V., Singh, R., Singh, A. K., & Singh, S. K. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3289184 · Cited by 15
MFLCES: Multi-Level Federated Edge Learning Algorithm Based on Client and Edge Server Selection
Liu, Z., Duan, S., Wang, S., Liu, Y., & Li, X. (2023) · Electronics · doi:10.3390/electronics12122689 · Cited by 13
Deep Learning-Based Modeling of 5G Core Control Plane for 5G Network Digital Twin
Tao, Z., Guo, Y., He, G., Huang, Y., & You, X. (2023) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2023.3324634 · Cited by 13
On Intelligent Placement Decision-Making Algorithms for Wireless Digital Twin Networks via Bandit Learning
Gu, J., Fu, Y., & Hung, K. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3360959 · Cited by 16
Semi-supervised Federated Learning for Digital Twin 6G-enabled IIoT: A Bayesian estimated approach
Qi, Y., & Hossain, M. S. (2024) · Journal of Advanced Research · doi:10.1016/j.jare.2024.02.012 · Cited by 14
Access-Side DDoS Defense for Space-Air-Ground Integrated 6G V2X Networks
Chen, X., Feng, W., Chen, Y., Ge, N., & He, Y. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3393752 · Cited by 17
Securing Distributed Network Digital Twin Systems Against Model Poisoning Attacks
Zhang, Z., Fang, M., Chen, M., Li, G., Lin, X., & Liu, Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3421895 · Cited by 16
Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network
Chu, S., Li, J., Wang, J., Ni, Y., Wei, K., Chen, W., & Jin, S. (2025) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2025.3574143 · Cited by 14
Adaptive Decentralized Federated Learning in Resource-Constrained IoT Networks
Du, M., Zheng, H., Gao, M., & Feng, X. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3329128 · Cited by 12
6G Network Architecture Based on Digital Twin: Modeling, Evaluation, and Optimization
Wang, X., Liu, C., He, J., Shi, N., Zhang, T., & Pan, X. (2023) · IEEE Network · doi:10.1109/mnet.2023.3333822 · Cited by 12
ODTRA-Based Task Offload Optimization for Industrial Internet of Things: Improving Efficiency and Performance With Digital Twins and Metaheuristic Optimization
Dhivya, S., Rajagopalan, A., Nidumolu, V., Alroobaea, R., Kotb, H., AboRas, K. M., & Elrashidi, A. (2024) · IEEE Access · doi:10.1109/access.2024.3385636 · Cited by 12
Generative AI-Assisted Mobile-Edge Computation Offloading in Digital-Twin-Enabled IIoT
Zhuansun, C., Li, P., Liu, Y., & Tian, Z. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3547370 · Cited by 12
Convergence Analysis and Latency Minimization for Semi-Federated Learning in Massive IoT Networks
Ren, J., Ni, W., Tian, H., & Nie, G. (2023) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2023.3309657 · Cited by 12
Sustainable Placement With Cost Minimization in Wireless Digital Twin Networks
Zhou, Y., Fu, Y., Shi, Z., Hung, K., Quek, T. Q. S., & Zhang, Y. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3463671 · Cited by 11
Self-citation
Joint Task Coding and Transfer Optimization for Edge Computing Power Networks
Liu, J., Lu, Y., Wu, H., Ai, B., Jamalipour, A., & Zhang, Y. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3554100 · Cited by 11
Self-citation
Energy-Efficient Federated Learning and Migration in Digital Twin Edge Networks
Zhou, Y., Fu, Y., Shi, Z., Yang, H. H., Hung, K., & Zhang, Y. (2025) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2025.3567096 · Cited by 10
Self-citation
Trustworthy DNN partition for blockchain-enabled digital twin in wireless IIoT networks
Deng, X., Li, J., Shi, L. X., Wei, K., Ding, M., Shao, Y., Chen, W., & Jin, S. (2024) · Science China Information Sciences · doi:10.1007/s11432-024-4159-2 · Cited by 9
Dynamic Resource Scheduling for Deterministic Communication, Computation, and Control Integration in Industrial Cyber–Physical Systems
Zhang, W., Sun, T., Yang, D., Luan, T. H., & Zhang, H. (2025) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2025.3570438 · Cited by 9
Fluid Antenna-Enabled Near-Field Integrated Sensing, Computing, and Semantic Communication for Emerging Applications
Yang, Y., Zhou, J., Yang, Z., & Shikh‐Bahaei, M. (2025) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2025.3592294 · Cited by 10
Federated-Learning-Based Synchrotron X-Ray Microdiffraction Image Screening for Industry Materials
Chen, B., Xu, K., Zhu, Y., Tian, L., & Chang, V. I. C. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3205372 · Cited by 9
Digital Twins for Smart Home Gadget Threat Prediction using Deep Convolution Neural Network
Padmapriya, V., & Srivenkatesh, M. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140270 · Cited by 9
Adaptive Personalized Federated Learning With One-Shot Screening
Ge, Y., Zhou, Y., & Jia, L. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3346900 · Cited by 9
Enabling Real-Time Digital Twin in Social IoT System Through Personalized Federated Learning
Luo, T., Zhang, H., Cao, H., Abdellatif, T. M., & Moussa, S. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3590746 · Cited by 8
Breakout Local Search for Load-Balanced Federated Learning in Multi-BS Networks
Kato, M., Yuan, X., Rodrigues, T. K., Tang, F., Zhao, M., & Kato, N. (2026) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2026.3667101 · Cited by 8
Federated Learning for Enablement of Digital Twin
Patwardhan, A., Thaduri, A., Karim, R., & Arranz, M. C. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.04.179 · Cited by 8
Client Selection and Resource Allocation for Federated Learning in Digital-Twin-Enabled Industrial Internet of Things
He, S., Ren, T., Jiang, X., & Xu, M. (2023) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc55385.2023.10118927 · Cited by 6
AMFL: Resource-Efficient Adaptive Metaverse-Based Federated Learning for the Human-Centric Augmented Reality Applications
Qiao, D., Qian, L., Guo, S., Zhao, J., & Zhou, P. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3409446 · Cited by 7
Optimal directed acyclic graph federated learning model for energy-efficient IoT communication networks
Nalinipriya, G., Lydia, E. L., Sree, S. R., Nikolenko, D., Potluri, S., Ramesh, J. V. N., & Jayachandran, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-71995-y · Cited by 7
IC2S-Swarm: When Digital Twin Meets Collaborative ISR
Zhao, M., Li, X., Huang, Y., Li, H., Zhang, B., & Peng, M. (2025) · IEEE Communications Magazine · doi:10.1109/mcom.001.2400145 · Cited by 8
Graph-Enhanced Explainable Asynchronous Federated Learning for Internet of Vehicles
Zhou, H., Zhou, Y., & Yang, W. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3556933 · Cited by 7
A Novel Secure Split Federated Semantic Learning Framework and its Optimization for Digital Twin Network Evolution
Okegbile, S. D., Gao, H., & Cai, J. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3599838 · Cited by 8
Resilient Federated Learning for Vehicular Networks: A Digital Twin and Blockchain-Empowered Approach
Li, J., Zheng, C., & Chen, Z. (2025) · Future Internet · doi:10.3390/fi17110505 · Cited by 7
Mutual Knowledge-Distillation-Based Federated Learning for Short-Term Forecasting in Electric IoT Systems
Tong, C., Zhang, L., Ding, Y., & Yue, D. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3416527 · Cited by 7
Toward Industrial Densely Packed Object Detection: A Federated Semi-Supervised Learning Approach
Zhao, C., Gao, Z., Bao, S., & Xiao, K. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3443112 · Cited by 7
Mapping Wireless Networks into Digital Reality through Joint Vertical and Horizontal Learning
Zhang, Z., Chen, M., Yang, Z., & Liu, Y. (2024) · IFIP Networking Conference (IFIP Networking) · doi:10.23919/ifipnetworking62109.2024.10619056 · Cited by 8
Weight-Based Privacy-Preserving Asynchronous SplitFed for Multimedia Healthcare Data
Stephanie, V., Khalil, I., & Atiquzzaman, M. (2024) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3695876 · Cited by 9
DFL topology optimization based on peer weighting mechanism and graph neural network in digital twin platform
Tuan, N. A., Rizwan, A., Moe, S. J. S., Khan, A. N., & Kim, D. H. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-01887-9 · Cited by 7
Green Offloading and Trajectory Scheduling of Rechargeable UAVs in Aerial Edge Networks
Zhang, K., Cao, J., Wang, L., & Zhang, Y. (2022) · Globecom · doi:10.1109/globecom48099.2022.10001727 · Cited by 7
Self-citation
Accelerating Wireless Federated Learning With Adaptive Scheduling Over Heterogeneous Devices
Li, Y., Qin, X., Han, K., Ma, N., Xu, X., & Zhang, P. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3292494 · Cited by 7
Energy Efficient Digital Twin with Federated Learning via Non-orthogonal Multiple Access Transmission
Wang, T., Huang, N., Dai, M., Wu, Y., Qian, L., & Lin, B. (2022) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2022-spring54318.2022.9860888 · Cited by 7
A Comprehensive Survey on Digital Twin for Future Networks and Emerging Iot IndustryPreprint
Hakiri, A., Gokhale, A. S., Yahia, S. B., & Mellouli, N. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4535810 · Cited by 7
Ameliorating clustered federated learning using real-coded genetic algorithm
Chaudhary, R. K., Kumar, R., & Saxena, N. (2025) · Cluster Computing · doi:10.1007/s10586-025-05163-w · Cited by 7
A Survey on Collaboration Computing for Industrial Internet of Things: Digital Twin, Federated Learning, and Swarm Learning
Zhao, P., Su, N., Wang, Y., Sun, S., & Li, X. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3633747 · Cited by 6
TrustFLow: A traceable federated learning framework to enable trustworthy digital twins
Romandini, N., Costagliola, A. R., Bujari, A., & Montanari, R. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.108267 · Cited by 8
A digital twin modeling framework with graphical software for rapid development of aircraft assembly systems
Kang, R., Hu, J., Li, Z., Wang, L., Yang, J., & Tian, W. (2025) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2025.103213 · Cited by 6
Where Are We Now?—Exploring the Metaverse Representations to Find Digital Twins
Cruz, M., & Oliveira, A. (2024) · Electronics · doi:10.3390/electronics13101984 · Cited by 8
Control and Communication Coordination for Industrial Digital Twins of Sintering Process
Chen, C., Wen, X., Bai, X., Xu, L., Ren, C., Ye, J., Ma, Y., & Guan, X. (2022) · Wireless networks · doi:10.1007/978-3-030-98064-1_15 · Cited by 6
Joint Incentive Mechanism Design and Energy-Efficient Resource Allocation for Federated Learning in UAV-Assisted Internet of Vehicles
Lin, S., Li, Y., Han, Z., Zhuang, B., Ma, J., & Tianfield, H. (2024) · Drones · doi:10.3390/drones8030082 · Cited by 6
System-Level Digital Twin Modeling for Underwater Wireless IoT Networks
Wang, L., Yan, L., Li, X., & Han, S. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse13010032 · Cited by 5
AI-Driven Resource Management for Heterogeneous Industrial IoT: Challenges and Opportunities
Zheng, K., Yang, H., Lei, L., Mei, J., & Wu, B. (2025) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2025.3640688 · Cited by 5
Perspectives of Digital Twin-Empowered Distributed Artificial Intelligence for Edge Computing
Tran‐Dang, H., & Kim, D. (2023) · International Conference on Artificial Intelligence Innovation (ICAII) · doi:10.1109/icaii59460.2023.10497436 · Cited by 6
Mobility-Assisted Digital Twin Network Optimization over Industrial Internet of Things
Lee, S.-H., & Park, S. (2024) · Applied Sciences · doi:10.3390/app14199090 · Cited by 5
Differentially Private and Communication-Efficient Federated Learning for AIoT: The Perspective of Denoising and Sparsification
Li, L., Liu, Z. C., Sun, X., Chang, L., Lan, R., Li, J., & Wang, J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3520827 · Cited by 5

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks