【Author】
Ayaz, Ferheen; Sheng, Zhengguo; Tian, Daxin; Guan, Yong Liang
【Source】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.
【Keywords】Biological system modeling; Blockchains; Relays; Fuzzy logic; Economics; Data models; Analytical models; Blockchain; federated learning; smart contract
【摘要】车辆之间的信息交换在确保道路安全方面发挥着重要作用。紧急消息的传播通常通过广播进行。然而，高车辆密度和移动性导致消息传播面临广播风暴和数据包接收概率低等挑战。本文提出了一种基于联邦学习的区块链辅助消息传播解决方案。类似于区块链中基于激励的工作证明共识，车辆通过处理嵌入在区块链智能合约中的联邦学习证明（PoFL）共识，竞争成为中继节点（矿工）。对所提出的解决方案进行了理论和实践分析。具体来说，所提议的基于区块链的联邦学习导致更多车辆在给定时间内上传其模型，与不使用区块链的相同解决方案相比，这可能会在更短的时间内产生更准确的模型。它在减少 65.2% 的共识时间延迟、提高至少 8.2% 的消息传递率和更有效地保护邻居车辆的隐私方面也优于其他区块链方法。使用 Stackelberg 博弈进一步分析了激励车辆参与联邦学习和消息传播的经济模型。渐近复杂度的分析证明，与车载网络中的其他共识算法相比，PoFL 是最具可扩展性的解决方案。
【关键词】生物系统建模；区块链；继电器；模糊逻辑;经济学;数据模型；分析模型；区块链；联邦学习；智能合约
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