Federated Learning in 6G Non-Terrestrial Network for IoT Services: From the Perspective of Perceptive Mobile Network

被引：4
作者：
Mu, Junsheng [1 ]
Cui, Yuanhao [1 ]
Ouyang, Wenjiang [1 ]
Yang, Zhaohui [2 ]
Yuan, Weijie [3 ]
Jing, Xiaojun [1 ]
机构：
[1] Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China
[2] Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou 310007, Peoples R China
[3] Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen 518060, Peoples R China
来源：
IEEE NETWORK | 2024年 / 38卷 / 04期
关键词：
Sensors; Internet of Things; Robot sensing systems; 6G mobile communication; Downlink; Uplink; Data privacy; Federated learning; Federated Learning; Non-Terrestrial Network; Perceptive Mobile Network; IoT services;
D O I：
10.1109/MNET.2024.3380647
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Recently, federated learning (FL) has been a hotspot for its capacity of data privacy protection and excellent performance under few-shot conditions for Internet of Things (IoT) services. Meanwhile, 6G non-terrestrial network (NTN) provides an effective and affordable option for enhancing IoT device connectivity. When FL meets NTN, various challenges and opportunities will emerge to promote technological evolution in the field of IoT services. Motivated by this, this paper investigates the present situations of FL in NTN from the perspective of perceptive mobile network (PMN), and discusses the open challenges for FL-assisted PMN. Additionally, current opportunities are concluded from three aspects, including sensing and communication (S&C) aided learning, S&C as a task, and edge intelligence. Finally, the future directions are exploited and analyzed. This paper overviews NTN from the perspective of PMN and proposes the framework of sensing assisted FL in NTN. We hope that this article will provide some inspirations for FL and wireless communication researchers.
引用
收藏
页码：72 / 79
页数：8
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