Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges
Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges
复制标题
DOI：
10.1109/mcom.001.1900461
复制
发表时间：
2020-06-01
影响因子：
11.2
通讯作者：
Reed, Jeffrey H.
中科院分区：
文献类型：
--
作者：
Niknam, Solmaz;Dhillon, Harpreet S.;Reed, Jeffrey H.
无线通信社区越来越有兴趣用基于数据驱动的机器学习（ML）解决方案来补充传统的模型驱动设计方法。虽然传统的ML方法依赖于在中央实体中具有数据和处理头的假设，但这在无线通信应用中并不总是可行的，因为私有数据的不可访问性以及将原始数据传输到中央ML处理器所需的大量通信开销。因此，将数据保存在生成位置的分散式ML方法更具吸引力。由于其隐私保护的性质，联邦学习与许多无线应用特别相关，特别是在第五代（5G）网络的背景下。在本文中，我们对联邦学习的一般思想进行了简单介绍，讨论了5G网络中的几种可能应用，并描述了无线通信背景下联邦学习未来研究的关键技术挑战和开放问题。
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.