摘要
本文综述了机器学习(Machine learning,ML)在无线边缘网络的主要应用、典型学习方法、以及性能潜力。首先分析了无线边缘智能与传统人工智能的区别。而后讨论了两种降低训练ML复杂度的思路,一种是从学习方法角度研究知识与数据联合驱动的ML,另一种是从无线系统角度设计合适的训练和决策方法,分析了集中式决策和分布式决策、集中式训练和分布式训练的优缺点。进一步介绍了联邦学习在无线边缘网络中的应用现状和适用场景,总结了在降低通信开销和个性化学习方面的研究进展与存在的问题。最后对全文进行了总结。
This article reviews the main applications,typical learning techniques,and the potential of machine learning(ML)for wireless edge networks.We first analyze the difference between wireless edge intelligence and traditional artificial intelligence.Then,we discuss two approaches to reduce training complexity.One is knowledge-and-data driven ML,which is from the perspective of learning techniques.The other is to design appropriate way of training and decision-making,which is from the perspective of wireless systems.The pros and cons of centralized decision-making and distributed decision-making,centralized training and distributed training are analyzed.We proceed to discuss the application and scope of federated learning for wireless edge networks,and summarize the state-of-the-art and open problems in terms of reducing communication overhead and personalized learning.Finally,we conclude the paper.
作者
刘婷婷
杨晨阳
索士强
黄远芳
Liu Tingting;Yang Chenyang;Suo Shiqiang;Huang Yuanfang(School of Electronic and Information Engineering,Beihang University,Beijing 100191,China;Datang Mobile Communications Equipment Co.,Ltd,Beijing 100191,China)
出处
《信号处理》
CSCD
北大核心
2020年第11期1789-1803,共15页
Journal of Signal Processing
基金
国家自然科学基金(61671036)。
