A Decentralized Federated Learning Approach for Connected Autonomous Vehicles
Open accessAbstract
In this paper, we propose an autonomous blockchain-based federated learning (BFL) design for privacy-aware and efficient vehicular communication networking, where local on-vehicle machine learning (oVML) model updates are exchanged and verified in a distributed fashion. BFL enables on-vehicle machine learning without any centralized training data or coordination by utilizing the consensus mechanism of the blockchain. Relying on a renewal reward approach, we develop a mathematical framework that features the controllable network and BFL parameters, such as the retransmission limit, block size, block arrival rate, and the frame sizes, so as to capture their impact on the system-level performance. More importantly, our rigorous analysis of oVML system dynamics quantifies the end-to-end delay with BFL, which provides important insights into deriving optimal block arrival rate by considering communication and consensus delays.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
FL provides a privacy-preserving method to preserve the security and privacy of the hospital system while also providing the benefits of machine learning models to solve numerous difficulties related to healthcare systems.
“As a result, to preserve the security and privacy of the hospital system, FL provides a privacy-preserving method while also providing the benefits of machine learning models to solve numerous difficulties related to healthcare systems [193, 210].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [210]
Cites
Bitcoin: A Peer-to-Peer Electronic Cash SystemPreprint
Nakamoto, S. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3440802 · Cited by 14,599 · see citing works
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2986024 · Cited by 2,565 · 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
Federated Optimization: Distributed Machine Learning for On-Device Intelligence
Konečný, J., McMahan, H. B., Ramage, D., & Richtárik, P. (2016) · arXiv · doi:10.48550/arxiv.1610.02527 · Cited by 1,563 · see citing works
A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks
Chen, M., Yang, Z., Saad, W., Yin, C., Poor, H. V., & Cui, S. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3024629 · Cited by 1,547 · 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
Information propagation in the Bitcoin network
Decker, C., & Wattenhofer, R. P. (2013) · IEEE International Conference on Peer-to-Peer Computing (P2P) · doi:10.1109/p2p.2013.6688704 · Cited by 1,224 · see citing works
Blockchained On-Device Federated Learning
Kim, H., Park, J., Bennis, M., & Kim, S.-L. (2020) · IEEE Communications Letters · doi:10.1109/lcomm.2019.2921755 · Cited by 887 · see citing works
Mastering Bitcoin: Unlocking Digital Crypto-Currencies
Antonopoulos, A. M. (2014) · Cited by 867 · see citing works
Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges
Niknam, S., Dhillon, H. S., & Reed, J. H. (2020) · IEEE Communications Magazine · doi:10.1109/mcom.001.1900461 · Cited by 820 · see citing works
Eyal, I., & Sirer, E. G. (2018) · Communications of the ACM · doi:10.1145/3212998 · Cited by 747 · see citing works
Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications
Samarakoon, S., Bennis, M., Saad, W., & Debbah, M. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2956472 · Cited by 459 · see citing works
Shen, M., Tang, X., Zhu, L., Du, X., & Guizani, M. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2901840 · Cited by 443 · see citing works
Analysis of Bitcoin Pooled Mining Reward SystemsPreprint
Rosenfeld, M. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1112.4980 · Cited by 315 · see citing works
Sun, Y., Zhang, L., Feng, G., Yang, B., Cao, B., & Imran, M. A. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2905743 · Cited by 246 · see citing works
Analysis of Hashrate-Based Double SpendingPreprint
Rosenfeld, M. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1402.2009 · Cited by 234 · see citing works
Bitcoin blockchain dynamics: The selfish-mine strategy in the presence of propagation delay
Göbel, J., Keeler, P., Krzesinski, A. E., & Taylor, P. G. (2016) · Performance Evaluation · doi:10.1016/j.peva.2016.07.001 · Cited by 225 · see citing works
Chen, W., Zhang, Z., Hong, Z., Chen, C., Wu, J., Maharjan, S., Zheng, Z., & Zhang, Y. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2918296 · Cited by 153 · see citing works
Kumar, G., Saha, R., Rai, M. K., Thomas, R., & Kim, T.-H. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2911969 · Cited by 145 · see citing works
Feng, D., She, C., Ying, K., Lai, L., Hou, Z., Quek, T. Q. S., Li, Y., & Vucetic, B. S. (2019) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2019.2903657 · Cited by 104 · see citing works
Block arrivals in the Bitcoin blockchainPreprint
Bowden, R. A., Keeler, P., Krzesinski, A. E., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.07447 · Cited by 57 · see citing works
Corking by Forking: Vulnerability Analysis of Blockchain
Wang, S., Wang, C., & Hu, Q. (2019) · IEEE INFOCOM 2019 - IEEE Conference on Computer Communications · doi:10.1109/infocom.2019.8737490 · Cited by 39 · see citing works
Distilling On-Device Intelligence at the Network EdgePreprint
Park, J., Wang, S., Elgabli, A., Oh, S., Jeong, E., Cha, H., Kim, H., Kim, S., & Bennis, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.05895 · Cited by 28 · see citing works
Communication and Consensus Co-Design for Low-Latency and Reliable Industrial IoT Systems.Preprint
Seo, H., Park, J., Bennis, M., & Choi, W. (2019) · arXiv (Cornell University) · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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
Fu, Y., Li, C., Yu, F. R., Luan, T. H., & Zhao, P. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3297996 · Cited by 96 · see citing works
Guerreiro Augusto, M., Acar, B., Soto, A. C., Sivrikaya, F., & Albayrak, S. (2024) · Autonomous Intelligent Systems · doi:10.1007/s43684-023-00059-1 · Cited by 15 · see citing works
- Authors
- Pokhrel, S. R., & Choi, J.
- Affiliations
- Deakin University (AU)
- Venue
- 2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)
- Publisher
- IEEE
- Published
- 2020-04
- Pages
- 1-6
- OpenAlex
- W3037806987
- Cited by
- 102
- References
- 24
- Citations by year
- 2026: 4 · 2025: 13 · 2024: 18 · 2023: 33 · 2022: 13 · 2021: 19 · 2020: 2
- FWCI
- 12.70
- Type
- conference paper
- Language
- en
- Open access
- green
- figshare.com
- Licence
- Locations
- IEEE Wireless Communications and Networking Conference Workshops (WCNCW) (publishedVersion)
- Figshare (submittedVersion, cc-by)
- 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
- federated learning, blockchain, connected and autonomous vehicles, vehicular communication, decentralized learning, consensus algorithms, privacy preservation, end-to-end delay, retransmission limit, block size, frame size
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
