{"title":"TSFed:工业物联网网络中安全高效联合学习的三阶段优化机制","authors":"","doi":"10.1016/j.iot.2024.101287","DOIUrl":null,"url":null,"abstract":"<div><p>This paper presents a three-stage optimization mechanism designed to enhance Federated Learning (FL) in Industrial Internet of Things (IIoT) networks. Traditional FL optimizations, which typically focus on a single aspect, fall short in IIoT environments. Our approach integrates a multi-criteria enhancement: first, an Ensembled Client Selection Mechanism (ECSM) selects participants based on accuracy, reputation, and randomness. Second, Adaptive Distributed Client Training (ADCT) dynamically adjusts based on participant performance. Lastly, a Secure and Efficient Communication Channel (SECC), backed by blockchain, meets IIoT’s stringent security demands. The evaluation shows TSFed outperforms baseline methods, enhancing FL performance by increasing accuracy and F1-score. Importantly, TSFed improves the efficiency of achieving 80% accuracy on the MNIST dataset by 29.09% over baseline methods, showcasing significant gains in both security and efficiency. This mechanism also exhibits robustness against malicious attacks, setting a new benchmark for FL in IIoT environments.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"TSFed: A three-stage optimization mechanism for secure and efficient federated learning in industrial IoT networks\",\"authors\":\"\",\"doi\":\"10.1016/j.iot.2024.101287\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper presents a three-stage optimization mechanism designed to enhance Federated Learning (FL) in Industrial Internet of Things (IIoT) networks. Traditional FL optimizations, which typically focus on a single aspect, fall short in IIoT environments. Our approach integrates a multi-criteria enhancement: first, an Ensembled Client Selection Mechanism (ECSM) selects participants based on accuracy, reputation, and randomness. Second, Adaptive Distributed Client Training (ADCT) dynamically adjusts based on participant performance. Lastly, a Secure and Efficient Communication Channel (SECC), backed by blockchain, meets IIoT’s stringent security demands. The evaluation shows TSFed outperforms baseline methods, enhancing FL performance by increasing accuracy and F1-score. Importantly, TSFed improves the efficiency of achieving 80% accuracy on the MNIST dataset by 29.09% over baseline methods, showcasing significant gains in both security and efficiency. This mechanism also exhibits robustness against malicious attacks, setting a new benchmark for FL in IIoT environments.</p></div>\",\"PeriodicalId\":29968,\"journal\":{\"name\":\"Internet of Things\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2024-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Internet of Things\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2542660524002282\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internet of Things","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2542660524002282","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
TSFed: A three-stage optimization mechanism for secure and efficient federated learning in industrial IoT networks
This paper presents a three-stage optimization mechanism designed to enhance Federated Learning (FL) in Industrial Internet of Things (IIoT) networks. Traditional FL optimizations, which typically focus on a single aspect, fall short in IIoT environments. Our approach integrates a multi-criteria enhancement: first, an Ensembled Client Selection Mechanism (ECSM) selects participants based on accuracy, reputation, and randomness. Second, Adaptive Distributed Client Training (ADCT) dynamically adjusts based on participant performance. Lastly, a Secure and Efficient Communication Channel (SECC), backed by blockchain, meets IIoT’s stringent security demands. The evaluation shows TSFed outperforms baseline methods, enhancing FL performance by increasing accuracy and F1-score. Importantly, TSFed improves the efficiency of achieving 80% accuracy on the MNIST dataset by 29.09% over baseline methods, showcasing significant gains in both security and efficiency. This mechanism also exhibits robustness against malicious attacks, setting a new benchmark for FL in IIoT environments.
期刊介绍:
Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT.
The journal will place a high priority on timely publication, and provide a home for high quality.
Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.