Distributed Learning for Wireless Communications: Methods, Applications and Challenges

doi:10.1109/JSTSP.2022.3156756

Distributed Learning for Wireless Communications: Methods, Applications and Challenges

2022-04-01

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DOI：10.1109/JSTSP.2022.3156756

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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, research on distributed learning for wireless communications is 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 emerging wireless network paradigms are then provided. Finally, main research directions and challenges of distributed learning for wireless communications are discussed.

Keywords:

Servers

Distance learning

Computer aided instruction

Wireless communication

Machine learning

Signal processing algorithms

Computer architecture

Distributed learning

federated learning

wireless communications

Distributed Learning for Wireless Communications: Methods, Applications and Challenges

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