FedTSE: Low-Cost Federated Learning for Privacy-Preserved Traffic State Estimation in IoV

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

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

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

Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting

Li, M., & Zhu, Z. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i5.16542 · Cited by 1,005 · see citing works

Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

Cui, Z., Henrickson, K. C., Ke, R., & Wang, Y. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2950416 · Cited by 983 · see citing works

Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles

Lu, Y., Huang, X., Zhang, K., Maharjan, S., & Zhang, Y. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.2973651 · Cited by 733 · see citing works

Vehicular Edge Computing and Networking: A Survey

Liu, L., Chen, C., Pei, Q., Maharjan, S., & Zhang, Y. (2020) · Mobile Networks and Applications · doi:10.1007/s11036-020-01624-1 · Cited by 623 · see citing works

Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation

Chen, Y., Sun, X., & Jin, Y. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2953131 · Cited by 575 · see citing works

Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values

Cui, Z., Ke, R., Pu, Z., & Wang, Y. (2020) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2020.102674 · Cited by 490 · see citing works

Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems

Wahab, O. A., Mourad, A., Otrok, H., & Taleb, T. (2021) · IEEE Communications Surveys and Tutorials · doi:10.1109/comst.2021.3058573 · Cited by 444 · see citing works

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues

Du, Z., Wu, C., Yoshinaga, T., Yau, K.-L. A., Ji, Y., & Li, J. (2020) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2020.2992630 · Cited by 393 · see citing works

Deep learning models for traffic flow prediction in autonomous vehicles: a review, solutions, and challenges

Miglani, A., & Kumar, N. (2019) · Vehicular Communications · doi:10.1016/j.vehcom.2019.100184 · Cited by 295 · see citing works

Unified Biometric Privacy Preserving Three-Factor Authentication and Key Agreement for Cloud-Assisted Autonomous Vehicles

Jiang, Q. G., Zhang, N., Ni, J., Ma, J., Ma, X., & Choo, K. R. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.2971254 · Cited by 266 · see citing works

Blockchain-Enabled Secure Data Sharing Scheme in Mobile-Edge Computing: An Asynchronous Advantage Actor–Critic Learning Approach

Liu, L., Feng, J., Pei, Q., Chen, C., Ming, Y., Shang, B., & Dong, M. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3048345 · Cited by 189 · see citing works

ConSTGAT

Fang, X., Huang, J., Wang, F., Zeng, L., Liang, H., & Wang, H. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403320 · Cited by 167 · see citing works

Min-Max Cost Optimization for Efficient Hierarchical Federated Learning in Wireless Edge Networks

Feng, J., Liu, L., Pei, Q., & Li, K. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3131654 · Cited by 142 · see citing works

Vehicles Meet Infrastructure: Toward Capacity–Cost Tradeoffs for Vehicular Access Networks

Lu, N., Zhang, N., Cheng, N., Shen, X., Mark, J. W., & Bai, F. (2013) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2013.2258153 · Cited by 86 · see citing works

Machine-Learning-Based Efficient and Secure RSU Placement Mechanism for Software-Defined-IoV

Anbalagan, S., Bashir, A. K., Raja, G., Dhanasekaran, P., Vijayaraghavan, G., Tariq, U., & Guizani, M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3069642 · Cited by 84 · see citing works

Global-Local Temporal Convolutional Network for Traffic Flow Prediction

Ren, Y., Zhao, D. Y., Luo, D., Ma, H., & Duan, P. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3025076 · Cited by 65 · see citing works

A federated bidirectional connection broad learning scheme for secure data sharing in Internet of Vehicles

Yuan, X., Chen, J., Zhang, N., Fang, X., & Liu, D. (2021) · China Communications · doi:10.23919/jcc.2021.07.010 · Cited by 49 · see citing works

Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges

Chellapandi, V. P., Yuan, L., Brinton, C. G., Żak, S. H., & Wang, Z. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3332675 · Cited by 249 · see citing works

Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems

Zhang, S., Li, J., Shi, L. X., Ding, M., Nguyen, D. C., Tan, W., Weng, J., & Han, Z. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3324962 · Cited by 169 · see citing works

A Survey on Federated Learning in Intelligent Transportation Systems

Zhang, R., Mao, J., Wang, H., Li, B., Cheng, X., & Yang, L. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3446319 · Cited by 99 · see citing works

A distributed intrusion detection framework for vehicular Ad Hoc networks via federated learning and Blockchain

Mansouri, F., Tarhouni, M., Alaya, B., & Zidi, S. (2024) · Ad Hoc Networks · doi:10.1016/j.adhoc.2024.103677 · Cited by 51 · see citing works

Fed-NTP: A Federated Learning Algorithm for Network Traffic Prediction in VANET

Sepasgozar, S. S., & Pierre, S. (2022) · IEEE Access · doi:10.1109/access.2022.3221970 · Cited by 48 · see citing works

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

Yuan, X., Chen, J., Zhang, N., Ye, Q. J., Li, C., Zhu, C., & Shen, X. (2023) · Engineering · doi:10.1016/j.eng.2023.04.015 · Cited by 46 · see citing works

Federated MADDPG-Based Collaborative Scheduling Strategy in Vehicular Edge Computing

Lei, S., Tang, H., Li, C., Zhang, X., Xu, C., & Wu, H. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3590747 · Cited by 18 · see citing works

ShieldTSE : A Privacy-Enhanced Split Federated Learning Framework for Traffic State Estimation in IoV

Chen, T., Bai, X., Zhao, J., Wang, H., Du, B., Li, L., & Zhang, S. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3442922 · Cited by 18 · see citing works

A traffic flow prediction framework based on integrated federated learning and Recurrent Long short-term networks

Pulligilla, M. K., & Vanmathi, C. (2024) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-024-01792-x · Cited by 9 · see citing works

Distributed long-time series prediction based on multiple feature decomposition and federated meta-learning under data heterogeneity

Yang, M., Zhang, S., Wang, Z., & Wang, S. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111252 · Cited by 5 · see citing works

Unmanned aerial vehicle swarm-assisted reliable federated learning for traffic flow prediction

Zhou, M., Han, L., & Geng, Y. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.107828 · Cited by 4 · see citing works

Teleportation Links: Mitigating Catastrophic Forgetting in Decentralized Federated Learning

Wang, X., Chen, Y., Ye, Q. J., & Dobre, O. A. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3611109 · Cited by 3 · see citing works

Connectivity Enrichment for Decentralized Federated Learning Networks with Teleportation

Wang, X., Chen, Y., Ye, Q. J., & D, O. A. D. (2025) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps65812.2025.11152750 · Cited by 3 · see citing works

Blockchain-Oriented Certificateless Threshold Signature With Identifiable Abort for Federated Learning in Digital Twin-Assisted IoV

Hu, Y., Wang, Z., Xia, Y., Xiong, H., Fang, L., Peng, C., Wahaballa, A., Qin, Z., & Qin, Z. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3579613 · Cited by 2 · see citing works

Real-Time Urban Traffic State Estimation via UAV-Based Sensing: A Gaussian Process and Moving Horizon Estimation Approach

Theocharides, K., Englezou, Y., Menelaou, C., & Timotheou, S. (2026) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3648943 · Cited by 2 · see citing works

PPFedSL: Privacy Preserving Split and Federated Learning Enabled Secure Data Sharing Model for Internet of Vehicles in Smart City

Soares, K., Shinde, A. A., & Patil, M. V. (2024) · International Journal of Computer Networks And Applications · doi:10.22247/ijcna/2025/11 · Cited by 2 · see citing works

Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction

Islam, N., & Zulkernine, M. (2025) · arXiv · doi:10.48550/arxiv.2503.01089 · Cited by 1 · see citing works

Heterogeneous-Aware Traffic Prediction: A Privacy-Preserving Federated Learning Framework

Zeng, Z., Fang, Z., Huang, Y., Wang, Q., Chen, L., & Gao, Y. (2025) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65448.2025.00038 · Cited by 1 · see citing works

Federated Learning-based Techniques for Healthcare IoV: Challenges and Future Directions -A Knowledge Review

Narsimhulu, P., & Sahay, R. (2024) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct61223.2024.10544256 · Cited by 1 · see citing works

Universal and holistic privacy protection in quantum computing: a novel approach through quantum circuit equivalence homomorphic encryption

Zhang, X., Chang, Y., Lin, Z., Xue, W., Yan, L., & Zhang, S. (2024) · Quantum Science and Technology · doi:10.1088/2058-9565/ad749a · Cited by 1 · see citing works

UNIRE: Secure Trajectory-User Linking Model Aggregation with Knowledge Transfer

Tang, J., Zhu, H., Zheng, Y., Zhang, J., Wang, F., Zhao, J., & Li, H. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00134 · Cited by 1 · see citing works

FL-RDGRU: A Federal Learning Traffic Flow Prediction Without Memory Loss

Li, B., Wang, X., & Li, J. (2024) · International Conference on Artificial Intelligence and Computer Engineering (ICAICE) · doi:10.1109/icaice63571.2024.10864317

Lightweight Temporal Transformer Decomposition for Federated Autonomous Driving

Do, T., Nguyen, B. X., Tran, Q. D., Tjiputra, E., Chiu, T.-C., & Nguyen, A. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246435

ShieldTSE : A Privacy-Enhanced Split Federated Learning Framework for Traffic State Estimation in IoV

Chen, T., Bai, X., Zhao, J., Wang, H., Du, B., Li, L., & Zhang, S. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3442922 · Cited by 18 · see citing works

Privacy-preserving data fusion for traffic state estimation: A vertical federated learning approach

Wang, Q., & Yang, K. (2024) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2024.104743 · Cited by 27 · see citing works

vLOFT: Variable Learning Rate for Online Federated Traffic Prediction

Meese, C., Lee, D. H., Chen, H., & Nejad, M. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10422287 · Cited by 3 · see citing works

Reliable Traffic State Estimation via Vertical Federated Learning

Zhan, Z., Dong, Y., Doe, D. M., Hu, Y., Cao, S., & Han, Z. (2025) · International Conference on Communications · doi:10.1109/icc52391.2025.11161974

Federated deep reinforcement learning for privacy-preserving offloading in vehicular edge computing

Momani, A. M., Alsekait, D. M., Al-Khasawneh, M. A., Othman, S. H., Alkayid, K., Kouki, S., & Baalamurugan, K. M. (2026) · Scientific Reports · doi:10.1038/s41598-026-56587-2 · Cited by 2 · see citing works