A blockchain based federated learning for message dissemination in vehicular networks
Message exchange among vehicles plays an important role in ensuring road safety. Emergency message dissemination is usually carried out by broadcasting. However, high vehicle density and mobility lead to challenges in message dissemination such as broadcasting storm and low probability of packet rec...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/162521 |
| _version_ | 1824456966946160640 |
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| author | Ayaz, Ferheen Sheng, Zhengguo Tian, Daxin Guan, Yong Liang |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Ayaz, Ferheen Sheng, Zhengguo Tian, Daxin Guan, Yong Liang |
| author_sort | Ayaz, Ferheen |
| collection | NTU |
| description | Message exchange among vehicles plays an important role in ensuring road safety. Emergency message dissemination is usually carried out by broadcasting. However, high vehicle density and mobility lead to challenges in message dissemination such as broadcasting storm and low probability of packet reception. This paper proposes a federated learning based blockchain-assisted message dissemination solution. Similar to the incentive-based Proof-of-Work consensus in blockchain, vehicles compete to become a relay node (miner) by processing the proposed Proof-of-Federated-Learning (PoFL) consensus which is embedded in the smart contract of blockchain. Both theoretical and practical analysis of the proposed solution are provided. Specifically, the proposed blockchain based federated learning results in more vehicles uploading their models in a given time, which can potentially lead to a more accurate model in less time as compared to the same solution without using blockchain. It also outperforms other blockchain approaches in reducing 65.2% of time delay in consensus, improving at least 8.2% message delivery rate and preserving privacy of neighbor vehicle more efficiently. The economic model to incentivize vehicles participating in federated learning and message dissemination is further analyzed using Stackelberg game. The analysis of asymptotic complexity proves PoFL as the most scalable solution compared to other consensus algorithms in vehicular networks. |
| first_indexed | 2025-02-19T04:02:30Z |
| format | Journal Article |
| id | ntu-10356/162521 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T04:02:30Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1625212022-10-26T06:53:16Z A blockchain based federated learning for message dissemination in vehicular networks Ayaz, Ferheen Sheng, Zhengguo Tian, Daxin Guan, Yong Liang School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Blockchain Federated Learning Message exchange among vehicles plays an important role in ensuring road safety. Emergency message dissemination is usually carried out by broadcasting. However, high vehicle density and mobility lead to challenges in message dissemination such as broadcasting storm and low probability of packet reception. This paper proposes a federated learning based blockchain-assisted message dissemination solution. Similar to the incentive-based Proof-of-Work consensus in blockchain, vehicles compete to become a relay node (miner) by processing the proposed Proof-of-Federated-Learning (PoFL) consensus which is embedded in the smart contract of blockchain. Both theoretical and practical analysis of the proposed solution are provided. Specifically, the proposed blockchain based federated learning results in more vehicles uploading their models in a given time, which can potentially lead to a more accurate model in less time as compared to the same solution without using blockchain. It also outperforms other blockchain approaches in reducing 65.2% of time delay in consensus, improving at least 8.2% message delivery rate and preserving privacy of neighbor vehicle more efficiently. The economic model to incentivize vehicles participating in federated learning and message dissemination is further analyzed using Stackelberg game. The analysis of asymptotic complexity proves PoFL as the most scalable solution compared to other consensus algorithms in vehicular networks. Agency for Science, Technology and Research (A*STAR) This work was supported in part by the European Union’s Horizon 2020 research, and innovation programme under the Marie Skłodowska-Curie under Grant 101006411, in part by the Newton Advanced Fellowship under Grant 62061130221, in part by the National Natural Science Foundation of China under Grants U20A20155, 61822101, and 62173012, in part by the Beijing Municipal Natural Science Foundation under Grant L191001, and in part by the A*STAR under its RIE2020 Advanced Manufacturing, and Engineering (AME) Industry Alignment Fund–Pre Positioning (IAF-PP) under Grant A19D6a0053. 2022-10-26T06:53:16Z 2022-10-26T06:53:16Z 2021 Journal Article Ayaz, F., Sheng, Z., Tian, D. & Guan, Y. L. (2021). A blockchain based federated learning for message dissemination in vehicular networks. IEEE Transactions On Vehicular Technology, 71(2), 1927-1940. https://dx.doi.org/10.1109/TVT.2021.3132226 0018-9545 https://hdl.handle.net/10356/162521 10.1109/TVT.2021.3132226 2-s2.0-85120781237 2 71 1927 1940 en A19D6a0053 IEEE Transactions on Vehicular Technology © 2021 IEEE. All rights reserved. |
| spellingShingle | Engineering::Electrical and electronic engineering Blockchain Federated Learning Ayaz, Ferheen Sheng, Zhengguo Tian, Daxin Guan, Yong Liang A blockchain based federated learning for message dissemination in vehicular networks |
| title | A blockchain based federated learning for message dissemination in vehicular networks |
| title_full | A blockchain based federated learning for message dissemination in vehicular networks |
| title_fullStr | A blockchain based federated learning for message dissemination in vehicular networks |
| title_full_unstemmed | A blockchain based federated learning for message dissemination in vehicular networks |
| title_short | A blockchain based federated learning for message dissemination in vehicular networks |
| title_sort | blockchain based federated learning for message dissemination in vehicular networks |
| topic | Engineering::Electrical and electronic engineering Blockchain Federated Learning |
| url | https://hdl.handle.net/10356/162521 |
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