面向6G工业物联网的联邦学习：从需求、愿景到挑战、机遇

刘淼; 夏雨虹; 赵海涛; 郭亮; 施政; 朱洪波

doi:10.11999/JEIT240574

面向6G工业物联网的联邦学习：从需求、愿景到挑战、机遇

doi: 10.11999/JEIT240574

刘淼¹,
夏雨虹¹,
赵海涛^1, ,,
郭亮²,
施政¹,
朱洪波¹

1.
南京邮电大学南京 210003
2.
中国信息通信研究院云计算与大数据研究所北京 100191

基金项目: 科技创新2030—“新一代人工智能”重大项目(2021ZD0113003)

详细信息

作者简介:
刘淼：男，博士，讲师，硕导，研究方向为认知无线网络、智能车联网、异构物联网、雾无线接入网、非正交多址技术、无人机通信、B5G/6G理论、基于模型驱动的深度学习技术等

夏雨虹：男，硕士生，研究方向为联邦学习、工业物联网、边缘计算等

赵海涛：男，博士，教授，博导，研究方向为智能网络技术、多信道建模技术、物联网、边缘计算等

郭亮：男，博士，正高级工程师，研究方向为网络、计算和存储等算力相关的研究和支撑工作

施政：男，博士，副教授，硕导，研究方向为可见光通信、半导体信息器件等

朱洪波：男，博士，教授，博导，研究方向为移动通信与宽带无线技术、无线通信与电磁兼容

通讯作者:
赵海涛　zhaoht@njupt.edu.cn

中图分类号: TN92
计量
- 文章访问数: 32
- HTML全文浏览量: 8
- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2024-07-08
- 修回日期: 2024-11-12
- 网络出版日期: 2024-11-29

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

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)

摘要

摘要: 随着6G技术的蓬勃发展和工业物联网的不断演进，联邦学习在工业领域的应用备受关注。因此，该文专注于探讨6G推动下工业物联网中联邦学习的发展与应用潜力，分析6G在工业物联网的应用前景，探索如何结合6G特性利用联邦学习技术满足数据隐私保护、资源优化和智能决策需求。首先，调研总结了现有相关工作，提出了联邦学习技术面向6G工业物联网应用场景的发展需求与愿景。在此基础上，构建了一种基于分层跨域架构的工业联邦学习新范式，旨在融合6G与数字孪生技术赋能实现泛在、灵活、层次化的联邦学习，以支撑典型工业物联网场景中按需、可靠的分布式智能业务，实现运营信息通信技术(OCIT)的融合。其次，分析归纳了面向6G工业物联网的联邦学习(6G IIoT-FL)可能面临的研究挑战，并提出了潜在的解决方案或建议。最后，指出了该技术未来值得关注的相关方向，旨在一定程度上为后续研究开拓思路。
- 工业物联网 /
- 联邦学习 /
- 6G /
- 数字孪生 /
- OICT融合
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

HTML全文

图 1 本文组织结构

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图 2 数字孪生驱动的6G IIoT-FL融合新范式

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图 3 物理域联邦学习与孪生域联邦学习的跨域融合机制

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图 4 6G IIoT-FL的关键挑战及相应对策

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图 5 基于融合新范式的6G IIoT-FL未来研究方向

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表 1 相关工作调研

参考文献	主题	贡献	尚未考虑
[11]	面向6G通信技术的工业5.0和信息物理系统	分析了6G技术在工业物联网和智能信息物理系统中存在的挑战与机遇，提出了相关解决方案	没有讨论联邦学习在其中的应用
[12]	面向联邦学习的工业物联网	具体介绍了无线联邦学习在工业物联网中的应用场景和方法，并分析了其优势和局限性	没有涉及到6G技术部分的探讨
[13]	面向联邦学习的工业物联网	讨论了无线联邦学习和工业物联网的融合应用，包括架构、算法和安全等方面，为实现6G无线联邦学习技术提供基础	没有重点讨论6G无线联邦学习技术
[14]	面向联邦学习的工业物联网	对联邦学习在工业物联网状态监测中的应用进行了全面的综述	未涉及6G网络的发展及其对联邦学习和工业过程状态监测的潜在影响
[15]	面向6G通信技术的联邦学习	分析了在6G通信场景下，如何利用无线联邦学习解决数据隐私和安全等问题，并探讨了未来发展方向	缺少讨论具体工业物联网应用场景

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