计算机科学
无线
架空(工程)
背景(考古学)
原始数据
补语(音乐)
钥匙(锁)
无线网络
分布式计算
计算机网络
电信
计算机安全
生物
基因
操作系统
表型
古生物学
化学
互补
程序设计语言
生物化学
作者
Solmaz Niknam,Harpreet S. Dhillon,Jeffrey H. Reed
出处
期刊:IEEE Communications Magazine
[Institute of Electrical and Electronics Engineers]
日期:2020-06-01
卷期号:58 (6): 46-51
被引量:571
标识
DOI:10.1109/mcom.001.1900461
摘要
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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