The IoT ecosystem is able to leverage vast amounts of data for intelligent decision-making. Federated Learning (FL), a decentralized machine learning technique, is widely used to collect and train machine learning models from a variety of distributed data sources. Both IoT and FL systems can be complementary and used together. However, the resource-constrained nature of IoT devices prevents the widescale deployment FL in the real world. This research paper presents a comprehensive survey of the challenges and solutions associated with implementing Federated Learning (FL) in resource-constrained Internet of Things (IoT) environments, viewed from 2 levels, client and server. We focus on solutions regarding limited client resources, presence of heterogeneous client data, server capacity, and high communication costs, and assess their effectiveness in various scenarios. Furthermore, we categorize the solutions based on the location of their application, i.e., the IoT client, and the FL server. In addition to a comprehensive review of existing research and potential future directions, this paper also presents new evaluation metrics that would allow researchers to evaluate their solutions on resource-constrained IoT devices.

翻译：物联网生态系统能够利用海量数据实现智能决策。联邦学习（Federated Learning, FL）作为一种去中心化机器学习技术，被广泛应用于从多种分布式数据源收集并训练机器学习模型。物联网与联邦学习系统具有互补性，可协同应用。然而，物联网设备的资源受限特性阻碍了联邦学习在现实场景中的大规模部署。本研究从客户端与服务器两个层面，系统综述了在资源受限的物联网环境中实施联邦学习所面临的挑战与解决方案。我们聚焦于客户端资源有限、异构客户端数据存在、服务器容量不足及高通信成本等问题的解决策略，并评估了这些方案在不同场景中的有效性。此外，我们依据解决方案的应用位置（即物联网客户端与联邦学习服务器）对其进行归类。除对现有研究与潜在未来方向进行全面综述外，本文还提出了新的评估指标，使研究者能够在资源受限的物联网设备上评估其解决方案的有效性。