{"title":"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","authors":"Qing Yang;Wei Xu;Taotao Wang;Hao Wang;Xiaoxiao Wu;Bin Cao;Shengli Zhang","doi":"10.1109/OJCOMS.2024.3471621","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6420-6429"},"PeriodicalIF":6.3000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10701002","citationCount":"0","resultStr":"{\"title\":\"Blockchain-Based Decentralized Federated Learning With On-Chain Model Aggregation and Incentive Mechanism for Industrial IoT\",\"authors\":\"Qing Yang;Wei Xu;Taotao Wang;Hao Wang;Xiaoxiao Wu;Bin Cao;Shengli Zhang\",\"doi\":\"10.1109/OJCOMS.2024.3471621\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":33803,\"journal\":{\"name\":\"IEEE Open Journal of the Communications Society\",\"volume\":\"5 \",\"pages\":\"6420-6429\"},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10701002\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Open Journal of the Communications Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10701002/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of the Communications Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10701002/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
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.
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.