The unprecedented surge of data volume in wireless networks empowered with artificial intelligence (AI) opens up new horizons for providing ubiquitous data-driven intelligent services. Traditional cloud-centric machine learning (ML)-based services are implemented by collecting datasets and training models centrally. However, this conventional training technique encompasses two challenges: (i) high communication and energy cost due to increased data communication, (ii) threatened data privacy by allowing untrusted parties to utilise this information. Recently, in light of these limitations, a new emerging technique, coined as federated learning (FL), arose to bring ML to the edge of wireless networks. FL can extract the benefits of data silos by training a global model in a distributed manner, orchestrated by the FL server. FL exploits both decentralised datasets and computing resources of participating clients to develop a generalised ML model without compromising data privacy. In this article, we introduce a comprehensive survey of the fundamentals and enabling technologies of FL. Moreover, an extensive study is presented detailing various applications of FL in wireless networks and highlighting their challenges and limitations. The efficacy of FL is further explored with emerging prospective beyond fifth generation (B5G) and sixth generation (6G) communication systems. The purpose of this survey is to provide an overview of the state-of-the-art of FL applications in key wireless technologies that will serve as a foundation to establish a firm understanding of the topic. Lastly, we offer a road forward for future research directions.
翻译:以人工智能(AI)为授权的无线网络的数据量空前激增,为提供无处不在的数据驱动智能服务开辟了新的视野;通过集中收集数据集和培训模型,实施了传统的云中机器学习(ML)服务;然而,这一常规培训技术包含两个挑战:(一) 由于数据通信增加,通信和能源成本高,(二) 通过允许不受信任的各方利用这一信息而威胁到数据隐私;(二) 鉴于这些限制,最近出现了一种新的新兴技术,即联结学习(FL),使ML进入无线网络的边缘;FL可以通过以分布方式培训全球模型来获取数据仓的好处,由FL服务器组织。FL利用分散的数据集和计算参与客户的资源,在不损害数据隐私的情况下开发一个通用的ML模型;在文章中,我们全面调查FL的基础和扶持技术。 此外,一项广泛的研究将详细介绍FL在无线网络中的多种应用,并突出其挑战和局限性。FL的效用是,FL的前瞻性研究基础,这是对未来技术的前瞻性研究基础。