Federated Learning in 6G Non-Terrestrial Network for IoT Services: From the Perspective of Perceptive Mobile Network-Reference-Cited by-同舟云学术

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

Published:2024-07 Issue:4 Volume:38 Page:72-79
ISSN:0890-8044
Container-title:IEEE Network
language:
Short-container-title:IEEE Network

Author:

Mu Junsheng¹^ORCID,Cui Yuanhao¹^ORCID,OUyang Wenjiang¹^ORCID,Yang Zhaohui²^ORCID,Yuan Weijie³^ORCID,Jing Xiaojun¹^ORCID

Affiliation:

1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China

2. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China

3. Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/65/10600457/10478029.pdf?arnumber=10478029

Reference15 articles.

1. A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks

2. Two-Layer Federated Learning With Heterogeneous Model Aggregation for 6G Supported Internet of Vehicles

3. Cell Selection with Deep Reinforcement Learning in Sparse Mobile Crowdsensing

4. Efficient Multilevel Federated Compressed Reinforcement Learning of Smart Homes Using Deep Learning Methods

5. FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robots

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1. Secure MIMO communication in energy harvesting-assisted NOMA Cognitive Radio Network with jamming under hardware impairment;Physical Communication;2024-10

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