Mengistu Tesfahunegn Minwuyelet, Kim Taewoon, Lin Jenn-Wei
Department of Information Convergence Engineering, Pusan National University, Busan 46241, Republic of Korea.
Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City 242062, Taiwan.
Sensors (Basel). 2024 Feb 1;24(3):968. doi: 10.3390/s24030968.
Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play a crucial role in IoT systems by collecting data from the physical environment. This paper presents a comprehensive survey of the integration of FL, IoT, and WSNs. It covers FL basics, strategies, and types and discusses the integration of FL, IoT, and WSNs in various domains. The paper addresses challenges related to heterogeneity in FL and summarizes state-of-the-art research in this area. It also explores security and privacy considerations and performance evaluation methodologies. The paper outlines the latest achievements and potential research directions in FL, IoT, and WSNs and emphasizes the significance of the surveyed topics within the context of current technological advancements.
联邦学习（FL）是一种机器学习（ML）技术，它能够在不共享原始数据的情况下进行协作式模型训练，这使其成为物联网（IoT）应用的理想选择，因为在物联网应用中，数据分布在各个设备上，并且隐私是一个需要关注的问题。无线传感器网络（WSN）通过从物理环境中收集数据，在物联网系统中发挥着至关重要的作用。本文对联邦学习、物联网和无线传感器网络的集成进行了全面综述。它涵盖了联邦学习的基础知识、策略和类型，并讨论了联邦学习、物联网和无线传感器网络在各个领域的集成。本文探讨了与联邦学习中的异构性相关的挑战，并总结了该领域的最新研究成果。它还探讨了安全和隐私方面的考虑因素以及性能评估方法。本文概述了联邦学习、物联网和无线传感器网络的最新成就和潜在研究方向，并强调了在所调查主题在当前技术进步背景下的重要性。
