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Article Dans Une Revue IEEE Journal of Selected Topics in Signal Processing Année : 2022

Distributed Learning for Wireless Communications: Methods, Applications and Challenges

Résumé

With its privacy-preserving and decentralized features, distributed learning plays an irreplaceable role in the era of wireless networks with a plethora of smart terminals, an explosion of information volume and increasingly sensitive data privacy issues. There is a tremendous increase in the number of scholars investigating how distributed learning can be employed to emerging wireless network paradigms in the physical layer, media access control layer and network layer. Nonetheless, researches on distributed learning for wireless communications are still in its infancy. In this paper, we review the contemporary technical applications of distributed learning for wireless communications. We first introduce the typical frameworks and algorithms for distributed learning. Examples of applications of distributed learning frameworks in the emerging wireless network paradigms are then provided. Finally, main research directions and challenges of distributed learning for wireless communications are discussed.
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Dates et versions

hal-03843797 , version 1 (08-11-2022)

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Liangxin Qian, Ping Yang, Ming Xiao, Octavia A Dobre, Marco Di Renzo, et al.. Distributed Learning for Wireless Communications: Methods, Applications and Challenges. IEEE Journal of Selected Topics in Signal Processing, 2022, 16 (3), ⟨10.1109/JSTSP.2022.3156756⟩. ⟨hal-03843797⟩
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