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

Junsheng MU; Yuanhao CUI; Wenjiang OUYANG; Zhaohui YANG; Weijie YUAN; Xiaojun JING

doi:10.1109/MNET.2024.3380647

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

Junsheng MU, Yuanhao CUI^*, Wenjiang OUYANG, Zhaohui YANG, Weijie YUAN, Xiaojun JING

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

2 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	72-79
Number of pages	8
Journal	IEEE Network
Volume	38
Issue number	4
Early online date	22 Mar 2024
DOIs	https://doi.org/10.1109/MNET.2024.3380647
Publication status	Published - Jul 2024
Externally published	Yes

Bibliographical note

Funding Agency:
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62301079)

Publisher Copyright:
© 1986-2012 IEEE.

Keywords

Federated Learning
IoT services
Non-Terrestrial Network
Perceptive Mobile Network

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10.1109/MNET.2024.3380647

Cite this

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

abstract = "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.",

keywords = "Federated Learning, IoT services, Non-Terrestrial Network, Perceptive Mobile Network",

author = "Junsheng MU and Yuanhao CUI and Wenjiang OUYANG and Zhaohui YANG and Weijie YUAN and Xiaojun JING",

note = "Funding Agency: 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62301079) Publisher Copyright: {\textcopyright} 1986-2012 IEEE.",

year = "2024",

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AU - MU, Junsheng

AU - CUI, Yuanhao

AU - OUYANG, Wenjiang

AU - YANG, Zhaohui

AU - YUAN, Weijie

AU - JING, Xiaojun

PY - 2024/7

Y1 - 2024/7

N2 - 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.

AB - 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.

KW - Federated Learning

KW - IoT services

KW - Non-Terrestrial Network

KW - Perceptive Mobile Network

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DO - 10.1109/MNET.2024.3380647

M3 - Journal Article (refereed)

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SN - 0890-8044

VL - 38

SP - 72

EP - 79

JO - IEEE Network

JF - IEEE Network

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ER -

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

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Bibliographical note

Keywords

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