基于区块链的去中心化联合学习与链上模型聚合和激励机制,适用于工业物联网

IF 6.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Qing Yang;Wei Xu;Taotao Wang;Hao Wang;Xiaoxiao Wu;Bin Cao;Shengli Zhang
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引用次数: 0

摘要

联合学习(FL)是一种新兴的机器学习模式,它能让参与者在不共享训练数据的情况下训练一个全局模型。最近,FL 因其数据私密性和可扩展性而被广泛应用于工业物联网场景。然而,当前的 FL 架构依赖于中央服务器来协调 FL 流程,因此存在隐私泄露和单点故障的风险。为了解决这个问题,我们提出了一种基于区块链技术的完全去中心化的 FL 架构。与现有的基于区块链的 FL 系统使用区块链进行协调或存储不同,我们使用区块链作为模型聚合的可信任计算平台。此外,我们还将 FL 任务发布者与参与者之间的互动建模为斯塔克尔伯格博弈,并设计了一种奖励机制来激励参与者为 FL 任务做出贡献。我们为所提出的去中心化 FL 架构构建了一个原型系统,并实施了一个基于 FL 的受损包裹检测应用,以评估所提出的方法。实验结果表明,基于区块链的去中心化 FL 在实际工业物联网场景中是可行的,激励机制在实际应用数据中表现良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Blockchain-Based Decentralized Federated Learning With On-Chain Model Aggregation and Incentive Mechanism for Industrial IoT
Federated learning (FL) is an emerging machine learning paradigm that enables the participants to train a global model without sharing the training data. Recently, FL has been widely deployed in industrial IoT scenarios because of its data privacy and scalability. However, the current FL architecture relies on a central server to orchestrate the FL process, thus incurring a risk of privacy leakage and single-point failure. To address this issue, we propose a fully decentralized FL architecture based on blockchain technology. Unlike existing blockchain-based FL systems that use blockchain for coordination or storage, we use blockchain as a trustable computing platform for model aggregation. Furthermore, we model the interaction between the FL task publisher and participants as a Stackelberg game and design a rewarding mechanism to incentivize participants to contribute to the FL task. We build a prototype system of the proposed decentralized FL architecture and implement an FL-based damaged package detection application to evaluate the proposed approach. Experimental results show that the blockchain-based decentralized FL is feasible in a practical industrial IoT scenario, and the incentive mechanism performs well with real application data.
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来源期刊
CiteScore
13.70
自引率
3.80%
发文量
94
审稿时长
10 weeks
期刊介绍: The IEEE Open Journal of the Communications Society (OJ-COMS) is an open access, all-electronic journal that publishes original high-quality manuscripts on advances in the state of the art of telecommunications systems and networks. The papers in IEEE OJ-COMS are included in Scopus. Submissions reporting new theoretical findings (including novel methods, concepts, and studies) and practical contributions (including experiments and development of prototypes) are welcome. Additionally, survey and tutorial articles are considered. The IEEE OJCOMS received its debut impact factor of 7.9 according to the Journal Citation Reports (JCR) 2023. The IEEE Open Journal of the Communications Society covers science, technology, applications and standards for information organization, collection and transfer using electronic, optical and wireless channels and networks. Some specific areas covered include: Systems and network architecture, control and management Protocols, software, and middleware Quality of service, reliability, and security Modulation, detection, coding, and signaling Switching and routing Mobile and portable communications Terminals and other end-user devices Networks for content distribution and distributed computing Communications-based distributed resources control.
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