【Author】
Lu, Yunlong; Huang, Xiaohong; Zhang, Ke; Maharjan, Sabita; Zhang, Yan
【Source】IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
【Abstract】In Internet of Vehicles (IoV), data sharing among vehicles for collaborative analysis can improve the driving experience and service quality. However, the bandwidth, security and privacy issues hinder data providers from participating in the data sharing process. In addition, due to the intermittent and unreliable communications in IoV, the reliability and efficiency of data sharing need to be further enhanced. In this paper, we propose a new architecture based on federated learning to relieve transmission load and address privacy concerns of providers. To enhance the security and reliability of model parameters, we develop a hybrid blockchain architecture which consists of the permissioned blockchain and the local Directed Acyclic Graph (DAG). Moreover, we propose an asynchronous federated learning scheme by adopting Deep Reinforcement Learning (DRL) for node selection to improve the efficiency. The reliability of shared data is also guaranteed by integrating learned models into blockchain and executing a two-stage verification. Numerical results show that the proposed data sharing scheme provides both higher learning accuracy and faster convergence.
【Keywords】Data sharing; Blockchain; Asynchronous federated learning; Deep reinforcement learning; Internet of Vehicles
【标题】区块链赋能异步联邦学习，实现车联网安全数据共享
【摘要】在车联网（IoV）中，车辆之间的数据共享以进行协同分析可以提高驾驶体验和服务质量。然而，带宽、安全和隐私问题阻碍了数据提供者参与数据共享过程。此外，由于车联网通信的间歇性和不可靠，数据共享的可靠性和效率需要进一步提高。在本文中，我们提出了一种基于联邦学习的新架构，以减轻传输负载并解决提供商的隐私问题。为了提高模型参数的安全性和可靠性，我们开发了一种混合区块链架构，该架构由许可区块链和本地有向无环图（DAG）组成。此外，我们提出了一种异步联邦学习方案，采用深度强化学习（DRL）进行节点选择以提高效率。通过将学习模型集成到区块链中并执行两阶段验证，也可以保证共享数据的可靠性。数值结果表明，所提出的数据共享方案提供了更高的学习精度和更快的收敛速度。
【关键词】数据共享；区块链；异步联邦学习；深度强化学习；车联网
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