Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles
Abstract
In Internet of Vehicles (IoV), data sharing among vehicles for collaborative analysis can improve the driving experience and service quality. However, the bandwidth, security and privacy issues hinder data providers from participating in the data sharing process. In addition, due to the intermittent and unreliable communications in IoV, the reliability and efficiency of data sharing need to be further enhanced. In this paper, we propose a new architecture based on federated learning to relieve transmission load and address privacy concerns of providers. To enhance the security and reliability of model parameters, we develop a hybrid blockchain architecture which consists of the permissioned blockchain and the local Directed Acyclic Graph (DAG). Moreover, we propose an asynchronous federated learning scheme by adopting Deep Reinforcement Learning (DRL) for node selection to improve the efficiency. The reliability of shared data is also guaranteed by integrating learned models into blockchain and executing a two-stage verification. Numerical results show that the proposed data sharing scheme provides both higher learning accuracy and faster convergence.
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The approach in [63] potentially reduces learning costs.
“This advanced approach potentially reduces learning costs with respect to agent selection and data sharing in task offloading.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [63]
Simulations with five RSUs and several vehicles demonstrate high performance regarding learning accuracy compared to non-cooperative learning approaches.
“Simulations with five RSUs and several vehicles demonstrate a high performance regarding learning accuracy compared to non-cooperative learning approaches [137].”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [63]
FL can offer innovative data offloading in vehicular networks in which vehicles and RSUs can share a common learning model to build a cooperative learning architecture, aiming to reduce learning costs with respect to agent selection and data sharing in task offloading.
“FL can also offer innovative data offloading in vehicular networks [63] in which vehicles and RSUs can share a common learning model to build a cooperative learning architecture, aiming to reduce learning costs with respect to agent selection and data sharing in task offloading.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [63]
Cites
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
An overview of gradient descent optimization algorithms
Ruder, S. (2016) · arXiv · doi:10.48550/arxiv.1609.04747 · Cited by 4,962 · see citing works
Federated Learning: Strategies for Improving Communication Efficiency
Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016) · arXiv · doi:10.48550/arxiv.1610.05492 · Cited by 3,766 · see citing works
Deterministic Policy Gradient Algorithms
Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., & Riedmiller, M. (2014) · HAL · Cited by 3,275 · see citing works
Blockchain for internet of things: a survey
Dai, H.-N., Zheng, Z., & Zhang, Y. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2920987 · Cited by 1,246 · see citing works
Self-citationBlockchain 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-citationKang, J., Yu, R., Huang, X., Maharjan, S., Zhang, Y., & Hossain, E. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2709784 · Cited by 1,141 · 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
Blockchain for Secure and Efficient Data Sharing in Vehicular Edge Computing and Networks
Kang, J., Yu, R., Huang, X., Wu, M., Maharjan, S., Xie, S., & Zhang, Y. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2875542 · Cited by 808 · see citing works
Self-citationInternet of Vehicles: Architecture, Protocols, and Security
Contreras-Castillo, J., Zeadally, S., & Guerrero-Ibanez, J. A. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2690902 · Cited by 744 · see citing works
Chen, M., Mozaffari, M., Saad, W., Yin, C., Debbah, M., & Hong, C. S. (2017) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2017.2680898 · Cited by 742 · see citing works
Device-to-Device Communication in LTE-Advanced Networks: A Survey
Liu, J., Kato, N., Ma, J., & Kadowaki, N. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2375934 · Cited by 643 · see citing works
Joint Load Balancing and Offloading in Vehicular Edge Computing and Networks
Dai, Y., Xu, D., Maharjan, S., & Zhang, Y. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2876298 · Cited by 467 · see citing works
Self-citationLu, Y., Huang, X., Dai, Y., Maharjan, S., & Zhang, Y. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2942179 · Cited by 387 · see citing works
Self-citationRodrigues, T. G., Suto, K., Nishiyama, H., & Kato, N. (2016) · IEEE Transactions on Computers · doi:10.1109/tc.2016.2620469 · Cited by 372 · see citing works
Relay-by-smartphone: realizing multihop device-to-device communications
Nishiyama, H., Ito, M., & Kato, N. (2014) · IEEE Communications Magazine · doi:10.1109/mcom.2014.6807947 · Cited by 366 · see citing works
Blockchain and Deep Reinforcement Learning Empowered Intelligent 5G Beyond
Dai, Y., Xu, D., Maharjan, S., Chen, Z., He, Q., & Zhang, Y. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800376 · Cited by 355 · see citing works
Self-citationEnergy Peer-to-Peer Trading in Virtual Microgrids in Smart Grids: A Game-Theoretic Approach
Anoh, K., Maharjan, S., Ikpehai, A., et al. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2934830 · Cited by 355 · see citing works
Self-citationDeep 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-citationData Security Sharing and Storage Based on a Consortium Blockchain in a Vehicular Ad-hoc Network
Zhang, X., & Chen, X. (2019) · IEEE Access · doi:10.1109/access.2018.2890736 · Cited by 285 · see citing works
Edge 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-citationTang, F., Fadlullah, Z. M., Kato, N., et al. (2017) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2017.2753280 · Cited by 266 · see citing works
Dai, 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-citationCooperative Content Caching in 5G Networks with Mobile Edge Computing
Zhang, K., Leng, S., He, Y., Maharjan, S., & Zhang, Y. (2018) · IEEE Wireless Communications · doi:10.1109/mwc.2018.1700303 · Cited by 250 · see citing works
Self-citationResource Trading in Blockchain-Based Industrial Internet of Things
Yao, H., Mai, T., Wang, J., Ji, Z., Jiang, C., & Qian, Y. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2902563 · Cited by 244 · see citing works
Liu, M., Yu, F. R., Teng, Y., Leung, V. C. M., & Song, M. N. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2866365 · Cited by 243 · see citing works
A Blockchain-Based Nonrepudiation Network Computing Service Scheme for Industrial IoT
Xu, Y., Ren, J., Wang, G., Zhang, C., Yang, J., & Zhang, Y. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2897133 · Cited by 230 · see citing works
Self-citationLiu, J., Nishiyama, H., Kato, N., et al. (2015) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2015.2452492 · Cited by 207 · see citing works
GANobfuscator: Mitigating Information Leakage Under GAN via Differential Privacy
Xu, C., Ren, J., Zhang, D., Zhang, Y., Qin, Z., & Ren, K. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2897874 · Cited by 207 · see citing works
Self-citationFederated Learning for Data Privacy Preservation in Vehicular Cyber-Physical Systems
Lu, Y., Huang, X., Dai, Y., Maharjan, S., & Yan, Z. (2020) · IEEE Network · doi:10.1109/mnet.011.1900317 · Cited by 191 · see citing works
Self-citationLiu, J., Zhang, S., Kato, N., Ujikawa, H., & Suzuki, K. (2015) · IEEE Network · doi:10.1109/mnet.2015.7166190 · Cited by 184 · see citing works
Rodrigues, T. K., Suto, K., Nishiyama, H., Kato, N., & Temma, K. (2018) · IEEE Transactions on Computers · doi:10.1109/tc.2018.2818144 · Cited by 167 · 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-citationVegvisir: A Partition-Tolerant Blockchain for the Internet-of-Things
Karlsson, K., -, W. J., Wicker, S. B., Adams, D., Ma, E., van Renesse, R., & Weatherspoon, H. (2018) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2018.00114 · Cited by 94 · see citing works
Enabling Trusted and Privacy-Preserving Healthcare Services in Social Media Health Networks
Tang, W., Ren, J., & Zhang, Y. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2889934 · Cited by 74 · see citing works
Self-citationBlockchain Empowered Wireless Power Transfer for Green and Secure Internet of Things
Jiang, L., Xie, S., Maharjan, S., & Zhang, Y. (2019) · IEEE Network · doi:10.1109/mnet.001.1900008 · Cited by 57 · see citing works
Self-citationEfficient and Privacy-preserving Fog-assisted Health Data Sharing Scheme
Tang, W., Ren, J., Zhang, K., Zhang, D., Zhang, Y., & Shen, X. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3341104 · Cited by 47 · see citing works
Self-citationUykan, Z., & Jäntti, R. (2015) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2015.2452511 · Cited by 46 · see citing works
Jiang, L., Xie, S., Maharjan, S., & Zhang, Y. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2950221 · Cited by 44 · see citing works
Self-citationAn asynchronous mini-batch algorithm for regularized stochastic optimization
Feyzmahdavian, H. R., Aytekin, A., & Johansson, M. (2015) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2015.7402404 · Cited by 15 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Federated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Vincent Poor, H. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3075439 · Cited by 1,503 · see citing works
“The research in [52] suggests an asynchronous federated data sharing framework for Internet of Vehicles (IoV) where each vehicle acts as an FL client to”
BackgroundMethodA survey on security and privacy of federated learning
Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.007 · Cited by 1,480 · see citing works
A Survey on Federated Learning for Resource-Constrained IoT Devices
Imteaj, A., Thakker, U., Wang, S., Li, J., & Amini, M. H. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3095077 · Cited by 774 · see citing works
Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications
Aledhari, M., Razzak, R., Parizi, R. M., & Saeed, F. (2020) · IEEE Access · doi:10.1109/access.2020.3013541 · Cited by 733 · see citing works
Federated 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
Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
Martínez Beltrán, E. T., Pérez, M. Q., Sánchez, P. M. S., Bernal, S. L., Bovet, G., Pérez, M. G., Pérez, G. M., & Celdrán, A. H. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3315746 · Cited by 625 · see citing works
“This mechanism has been utilized in several studies, such as [219] and [220].”
MethodFederated learning review: Fundamentals, enabling technologies, and future applications
Banabilah, S., Aloqaily, M., Alsayed, E., Malik, N., & Jararweh, Y. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103061 · Cited by 556 · 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
Blockchain-empowered Federated Learning: Challenges, Solutions, and Future Directions
Zhu, J., Cao, J., Saxena, D., Jiang, S., & Ferradi, H. (2023) · ACM Computing Surveys · doi:10.1145/3570953 · Cited by 309 · see citing works
Blockchain-enabled Federated Learning: A Survey
Qu, Y., Uddin, M. P., Gan, C., Xiang, Y., Gao, L., & Yearwood, J. (2023) · ACM Computing Surveys · doi:10.1145/3524104 · Cited by 286 · see citing works
Lakhan, A., Mohammed, M. A., Nedoma, J., Martinek, R., Tiwari, P., Vidyarthi, A., Alkhayyat, A., & Wang, W. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3165945 · Cited by 224 · see citing works
Drones’ Edge Intelligence Over Smart Environments in B5G: Blockchain and Federated Learning Synergy
Alsamhi, S. H., Almalki, F. A., Afghah, F., Hawbani, A., Shvetsov, A. V., Lee, B., & Song, H. (2022) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2021.3132561 · Cited by 139 · see citing works
Machine Learning for Security in Vehicular Networks: A Comprehensive Survey
Talpur, A., & Gurusamy, M. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3129079 · Cited by 107 · 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
Blockchain for Securing AI Applications and Open Innovations
Shinde, R., Patil, S., Kotecha, K., & Ruikar, K. (2021) · Journal of Open Innovation: Technology, Market, and Complexity · doi:10.3390/joitmc7030189 · Cited by 75 · see citing works
Blockchain-Based Personalized Federated Learning for Internet of Medical Things
Lian, Z., Wang, W., Han, Z., & Su, C. (2023) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2023.3279111 · Cited by 75 · see citing works
Bao, W., Wu, C., Guleng, S., Zhang, J., Yau, K.-L. A., & Ji, Y. (2021) · China Communications · doi:10.23919/jcc.2021.06.004 · Cited by 71 · see citing works
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging
Kumar, R., Khan, A. A., Kumar, J., Zakria, Golilarz, N. A., Zhang, S., Ting, Y., Zheng, C., & Wang, W. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3076767 · Cited by 57 · see citing works
Review on Federated Learning for digital transformation in healthcare through big data analytics
Babar, M., Qureshi, B., & Koubaa, A. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.05.046 · Cited by 55 · see citing works
Adam, M., & Baroudi, U. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3506214 · Cited by 46 · see citing works
Privacy Protection for Blockchain-Based Healthcare IoT Systems: A Survey
Qi, M., Wang, Z., Han, Q.-L., Zhang, J., Chen, S., & Xiang, Y. (2024) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.106058 · Cited by 44 · see citing works
Federated Learning-Based Cell-Free Massive MIMO System for Privacy-Preserving
Zhang, J., Zhang, J., Ng, D. W. K., & Ai, B. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3225812 · Cited by 30 · see citing works
Guo, H., Meese, C., Li, W., Shen, C.-C., & Nejad, M. (2023) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2023.3318990 · Cited by 26 · see citing works
Privacy-Preserving Solutions in Blockchain-Enabled Internet of Vehicles
Kaltakis, K., Polyzi, P., Drosatos, G., & Rantos, K. (2021) · Applied Sciences · doi:10.3390/app11219792 · Cited by 25 · see citing works
FedTSE: Low-Cost Federated Learning for Privacy-Preserved Traffic State Estimation in IoV
Yuan, X., Chen, J., Zhang, N., Zhu, C., Ye, Q., & Shen, X. S. (2022) · IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps54753.2022.9798018 · Cited by 22 · see citing works
A Blockchain-Based Privacy Information Security Sharing Scheme in Industrial Internet of Things
Wang, Y., Che, T., Zhao, X., Zhou, T., Zhang, K., & Hu, X. (2022) · Sensors · doi:10.3390/s22093426 · Cited by 20 · see citing works
Self-citationMachine Learning based Trust Model for Secure Internet of Vehicle Data Exchange
Manogaran, G., & Rawal, B. S. (2020) · 2020 IEEE Globecom Workshops (GC Wkshps · doi:10.1109/gcwkshps50303.2020.9367424 · Cited by 12 · see citing works
Islam, N., & Zulkernine, M. (2025) · arXiv · doi:10.48550/arxiv.2503.01089 · Cited by 1 · see citing works
“Additionally, managing cumulative privacy loss over multiple queries or iterations (the privacy budget) is crucial to maintaining robust privacy guarantees over time.”
- 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 Vehicular Technology
- Published
- 2020-04
- Volume
- 69
- Issue
- 4
- Pages
- 4298-4311
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W3006655855
- Cited by
- 733
- References
- 40
- Citations by year
- 2026: 53 · 2025: 108 · 2024: 144 · 2023: 173 · 2022: 120 · 2021: 94 · 2020: 41
- FWCI
- 100.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Vehicular Technology (publishedVersion)
- Funders
- National Natural Science Foundation of China and Xinjiang (U1603261)
- Key R&D Project of Sichuan Province (2019YFG0520)
- Opening Project of Shanghai Trusted Industrial Control Platform (2019YY201)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- Internet of Vehicles, federated learning, blockchain, asynchronous federated learning, data sharing, privacy preservation, permissioned blockchain, directed acyclic graphs, deep reinforcement learning, node selection, model verification, intermittent communication
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