Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162521
Title: A blockchain based federated learning for message dissemination in vehicular networks
Authors: Ayaz, Ferheen
Sheng, Zhengguo
Tian, Daxin
Guan, Yong Liang
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: 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
Project: A19D6a0053 
Journal: IEEE Transactions on Vehicular Technology
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 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.
URI: https://hdl.handle.net/10356/162521
ISSN: 0018-9545
DOI: 10.1109/TVT.2021.3132226
Rights: © 2021 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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