Ayaz, F. , Sheng, Z., Tian, D. and Guan, Y. L. (2022) A blockchain based federated learning for message dissemination in vehicular networks. IEEE Transactions on Vehicular Technology, 71(2), pp. 1927-1940. (doi: 10.1109/TVT.2021.3132226)
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Abstract
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 Proofof-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 neighbour vehicle more efficiently. The economic model to incentivize vehicles participating in federated learning and message dissemination is further analysed using Stackelberg game. The analysis of asymptotic complexity proves PoFL as the most scalable solution compared to other consensus algorithms in vehicular networks.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Ayaz, Ms Ferheen |
| Authors: | Ayaz, F., Sheng, Z., Tian, D., and Guan, Y. L. |
| College/School: | College of Science and Engineering > School of Computing Science |
| Journal Name: | IEEE Transactions on Vehicular Technology |
| Publisher: | IEEE |
| ISSN: | 0018-9545 |
| ISSN (Online): | 1939-9359 |
| Published Online: | 02 December 2021 |
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