The emergence of new services and applications in emerging wireless networks (e.g., beyond 5G and 6G) has shown a growing demand for the usage of artificial intelligence (AI) in the Internet of Things (IoT). However, the proliferation of massive IoT connections and the availability of computing resources distributed across future IoT systems have strongly demanded the development of distributed AI for better IoT services and applications. Therefore, existing AI-enabled IoT systems can be enhanced by implementing distributed machine learning (aka distributed learning) approaches. This work aims to provide a comprehensive survey on distributed learning for IoT services and applications in emerging networks. In particular, we first provide a background of machine learning and present a preliminary to typical distributed learning approaches, such as federated learning, multi-agent reinforcement learning, and distributed inference. Then, we provide an extensive review of distributed learning for critical IoT services (e.g., data sharing and computation offloading, localization, mobile crowdsensing, and security and privacy) and IoT applications (e.g., smart healthcare, smart grid, autonomous vehicle, aerial IoT networks, and smart industry). From the reviewed literature, we also present critical challenges of distributed learning for IoT and propose several promising solutions and research directions in this emerging area.

翻译：新兴无线网络（如5G演进及6G）中新型服务与应用的出现，彰显了人工智能在物联网领域日益增长的需求。然而，海量物联网连接的激增以及跨未来物联网系统分布式计算资源的可用性，强烈要求开发分布式人工智能以提供更优质的物联网服务与应用。因此，通过实施分布式机器学习方法，可增强现有基于AI的物联网系统。本文旨在对新兴网络中面向物联网服务与应用的分布式学习进行系统性综述。具体而言，我们首先介绍机器学习背景，并给出典型分布式学习方法（如联邦学习、多智能体强化学习、分布式推理）的初步阐述。继而，我们针对关键物联网服务（如数据共享与计算卸载、定位、群智感知、安全与隐私）及物联网应用（如智慧医疗、智能电网、自动驾驶、空中物联网、智慧工业）中的分布式学习展开广泛回顾。基于现有文献，本文还剖析了分布式学习在物联网领域面临的关键挑战，并提出了该新兴领域若干具有前景的解决方案和研究方向。