Federated Learning Technologies for 6G Industrial Internet of Things: From Requirements, Vision to Challenges, Opportunities

LIU Miao; XIA Yuhong; ZHAO Haitao; GUO Liang; SHI Zheng; ZHU Hongbo

doi:10.11999/JEIT240574

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LIU Miao, XIA Yuhong, ZHAO Haitao, GUO Liang, SHI Zheng, ZHU Hongbo. Federated Learning Technologies for 6G Industrial Internet of Things: From Requirements, Vision to Challenges, Opportunities[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240574

Citation:

LIU Miao, XIA Yuhong, ZHAO Haitao, GUO Liang, SHI Zheng, ZHU Hongbo. Federated Learning Technologies for 6G Industrial Internet of Things: From Requirements, Vision to Challenges, Opportunities[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240574

Citation:

LIU Miao, XIA Yuhong, ZHAO Haitao, GUO Liang, SHI Zheng, ZHU Hongbo. Federated Learning Technologies for 6G Industrial Internet of Things: From Requirements, Vision to Challenges, Opportunities[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240574

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Federated Learning Technologies for 6G Industrial Internet of Things: From Requirements, Vision to Challenges, Opportunities

doi: 10.11999/JEIT240574

1.
School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
Cloud Computing & Big Data Research Institute, The China Academy of Information and Communications Technology, Beijing 100191, China

Funds: The Scientific and Technological Innovation 2030 - "New Generation Artificial Intelligence" Major Project (2021ZD0113003)

Received Date: 2024-07-08
Rev Recd Date: 2024-11-12

Available Online: 2024-11-29

Abstract

Abstract

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 has explored 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 learning,
- 6G,
- Digital twin,
- Integration of OICT

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References(120)

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