Federated Learning Technologies for 6G Industrial Internet of Things:From Requirements,Vision to Challenges,Opportunities
With the rapid development of 6G technology and the evolution of the Industrial Internet of Things(IIoT),federated learning has gained significant attention in the industrial sector.This paper explores the development and application potential of federated learning in 6G-driven IIoT,analyzing 6G's prospects and how its high speed,low latency,and reliability can support data privacy,resource optimization,and intelligent decision-making.First,existing related work is summarized,and the development requirements along with the vision for applying federated learning technology in 6G industrial IoT scenarios are outlined.Based on this,a new paradigm for industrial federated learning,featuring a hierarchical cross-domain architecture,is proposed to integrate 6G and digital twin technologies,enabling ubiquitous,flexible,and layered federated learning.This supports on-demand and reliable distributed intelligent services in typical Industrial IoT scenarios,achieving the integration of Operational and Communication Information Technology(OCIT).Next,the potential research challenges that federated learning might face towards 6G industrial IoT(6G IIoT-FL)are analyzed and summarized,followed by potential solutions or recommendations.Finally,relevant future directions worth attention in this field are highlighted in the study,with the aim of providing insights for subsequent research to some extent.
Industrial Internet of Things(IIoT)Federated learning6GDigital twinIntegration of Operational and Communication Information Technology(OCIT)
万方数据
维普
