【Author】
Fan, Sizheng; Zhang, Hongbo; Zeng, Yuchen; Cai, Wei
【Source】IEEE INTERNET OF THINGS JOURNAL
【Abstract】By training a machine learning algorithm across multiple decentralized edge nodes, federated learning (FL) ensures the privacy of the data generated by the massive Internet-of-Things (IoT) devices. To economically encourage the participation of heterogeneous edge nodes, a transparent and decentralized trading platform is needed to establish a fair market among distinct edge companies. In this article, we propose a hybrid blockchain-based resource trading system that combines the advantages of both public and consortium blockchains. We design and implement a smart contract to facilitate an automatic, autonomous, and auditable rational reverse auction mechanism among edge nodes. Moreover, we leverage the payment channel technique to enable credible, fast, low-cost, and high-frequency payment transactions between requesters and edge nodes. Simulation results show that the proposed reverse auction mechanism can achieve the properties, including budget feasibility, truthfulness, and computational efficiency.
【Keywords】Blockchain; Internet of Things; Computational modeling; Edge computing; Peer-to-peer computing; Training; Smart contracts; Auction; blockchain; edge computing; Internet of Things (IoT); trade market
【标题】用于边缘计算联邦学习的基于混合区块链的资源交易系统
【摘要】通过跨多个去中心化边缘节点训练机器学习算法，联邦学习 (FL) 可确保由海量物联网 (IoT) 设备生成的数据的隐私性。为了在经济上鼓励异构边缘节点的参与，需要一个透明和去中心化的交易平台，在不同的边缘公司之间建立一个公平的市场。在本文中，我们提出了一种基于混合区块链的资源交易系统，它结合了公共区块链和联盟区块链的优势。我们设计并实施智能合约，以促进边缘节点之间自动、自主和可审计的合理反向拍卖机制。此外，我们利用支付通道技术在请求者和边缘节点之间实现可信、快速、低成本和高频的支付交易。仿真结果表明，所提出的反向拍卖机制可以实现预算可行性、真实性和计算效率等特性。
【关键词】区块链；物联网;计算建模；边缘计算；对等计算；训练;智能合约；拍卖;区块链；边缘计算；物联网（IoT）；交易市场
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