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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

刘淼, 夏雨虹, 赵海涛, 郭亮, 施政, 朱洪波. 面向6G工业物联网的联邦学习:从需求、愿景到挑战、机遇[J]. 电子与信息学报. doi: 10.11999/JEIT240574
引用本文: 刘淼, 夏雨虹, 赵海涛, 郭亮, 施政, 朱洪波. 面向6G工业物联网的联邦学习:从需求、愿景到挑战、机遇[J]. 电子与信息学报. doi: 10.11999/JEIT240574
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

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
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

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

Funds: The Scientific and Technological Innovation 2030 - "New Generation Artificial Intelligence" Major Project (2021ZD0113003)
  • 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。
  • 图  1  本文组织结构

    图  2  数字孪生驱动的6G IIoT-FL融合新范式

    图  3  物理域联邦学习与孪生域联邦学习的跨域融合机制

    图  4  6G IIoT-FL的关键挑战及相应对策

    图  5  基于融合新范式的6G IIoT-FL未来研究方向

    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
    下载: 导出CSV
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  • 收稿日期:  2024-07-08
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