【Author】
Zou, Yue; Shen, Fei; Yan, Feng; Lin, Jing; Qiu, Yunzhou
【Source】2021 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC)
【Abstract】The Internet of Vehicles (IoV) aims to perceive, compute, and process environmental data in a collaborative manner. Previous works focus on data sharing between vehicles, but a large amount of data will lead to redundant transmission and network congestion. In addition, security and privacy issues prevent these nodes from participating in the sharing process. Knowledge is extracted from data through machine learning (ML) and shared in the form of small-scale well-trained model parameters, which improves collaborative learning more effectively and relieves network pressure. While traditional ML algorithms are not suitable for distributed IoV with local characteristics. Based on this, this paper first divides the vehicles into multiple regions and proposes a Regional Federated Learning (RFL) framework, in which all regions maintain their own learning models, i.e. knowledge. We design a reputation mechanism to measure the reliability of vehicles participating in RFL. To address the security challenges brought by the untrusted centralized trading market, we propose a blockchain-enhanced knowledge trading framework, in which an authorized market agency coordinates the trading quickly. We model the optimal pricing mechanism as a non-cooperative game, taking into account the competition among all knowledge providers. Numerical simulation shows that the proposed reputation mechanism improves the accuracy of knowledge up to 18%, and the optimal knowledge pricing mechanism effectively increases the utility of market.
【Keywords】Regional Federated Learning; reputation; blockchain; Knowledge trading; pricing mechanism
【标题】区块链增强型车联网中基于信誉的区域联邦学习知识交易
【摘要】车联网 (IoV) 旨在以协作的方式感知、计算和处理环境数据。以往的工作侧重于车辆之间的数据共享，但大量的数据会导致冗余传输和网络拥塞。此外，安全和隐私问题阻止这些节点参与共享过程。通过机器学习（ML）从数据中提取知识，并以小规模训练好的模型参数的形式共享，更有效地提高了协作学习，缓解了网络压力。而传统的机器学习算法不适用于具有局部特征的分布式车联网。基于此，本文首先将车辆划分为多个区域，并提出了区域联邦学习（RFL）框架，其中所有区域都保持自己的学习模型，即知识。我们设计了一种信誉机制来衡量参与 RFL 的车辆的可靠性。为解决去中心化交易市场带来的安全挑战，我们提出了区块链增强的知识交易框架，授权市场机构快速协调交易。我们将最优定价机制建模为非合作博弈，同时考虑到所有知识提供者之间的竞争。数值模拟表明，所提出的信誉机制将知识的准确率提高了18%，最优的知识定价机制有效地增加了市场的效用。
【关键词】区域联邦学习；名声;区块链；知识交易；定价机制
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