FCLA-DT:Federated Continual Learning with Authentication for Distributed Digital Twin-based Industrial IoT  

作　　者：Yingjie Xia Xuejiao Liu Yunxiao Zhao Yun Wang 

机构地区：[1]Micro-Electronics Research Institute,Hangzhou Dianzi University,Hangzhou 310005,China [2]College of Computer Science and Technology,Zhejiang University,Hangzhou 310012,China [3]angzhou Yuantiao Technology Company,Hangzhou 310036,China [4]School of Information Science and Technology,Hangzhou Normal University,Hangzhou 311121,China [5]School of Cyberspace Security Technology,Hangzhou Dianzi University,Hangzhou 310018,China

出　　处：《Journal of Communications and Information Networks》2024年第4期362-373,共12页通信与信息网络学报(英文)

基　　金：supported by the National Natural Science Foundation of China under Grant 62472132;Natural Science Foundation of Zhejiang Province under Grant LZ22F030004;Key Research and Development Program Project of Zhejiang Province under Grant 2024C01179.

摘　　要：Digital twin(DT)technology is currently pervasive in industrial Internet of things(IoT)applications,notably in predictive maintenance scenarios.Prevailing digital twin-based predictive maintenance methodologies are constrained by a narrow focus on singular physical modeling paradigms,impeding comprehensive analysis of diverse factory data at scale.This paper introduces an improved method,federated continual learning with authentication for distributed digital twin-based industrial IoT(FCLA-DT).This decentralized strategy ensures the continual learning capability vital for adaptive and real-time decision-making in complex industrial predictive maintenance systems.An authentication scheme based on group signature is introduced to enable the verification of digital twin identities during inter-twin collaborations,avoiding unauthorized access and potential model theft.Security analysis shows that FCLA-DT can enable numerous nodes to collaborate learning without compromising individual twin privacy,thereby ensuring group authentication in the cooperative distributed industrial IoT.Performance analysis shows that FCLA-DT outperforms traditional federated learning methods with over 95% fault diagnosis accuracy and ensures the privacy and authentication of digital twins in multi-client task learning.

关 键 词：federated continual learning AUTHENTICATION group signature digital twin industrial Internet of things 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]