期刊
IEEE COMMUNICATIONS MAGAZINE
卷 58, 期 6, 页码 46-51出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MCOM.001.1900461
关键词
Training; Wireless communication; Data models; Distributed databases; Computational modeling; 5G mobile communication; Wireless sensor networks
资金
- U.S. NSF [CNS-1564148, CNS1814477, CNS-1642873]
There is a growing interest in the wireless communications community to complement the traditional model-driven design approaches with data-driven machine learning (ML)-based solutions. While conventional ML approaches rely on the assumption of having the data and processing heads in a central entity, this is not always feasible in wireless communications applications because of the inaccessibility of private data and large communication overhead required to transmit raw data to central ML processors. As a result, decentralized ML approaches that keep the data where it is generated are much more appealing. Due to its privacy-preserving nature, federated learning is particularly relevant for many wireless applications, especially in the context of fifth generation (5G) networks. In this article, we provide an accessible introduction to the general idea of federated learning, discuss several possible applications in 5G networks, and describe key technical challenges and open problems for future research on federated learning in the context of wireless communications.
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