The ability to monitor ambient characteristics, interact with them, and derive information about the surroundings has been made possible by the rapid proliferation of edge sensing devices like IoT, mobile, and wearable devices and their measuring capabilities with integrated sensors. Even though these devices are small and have less capacity for data storage and processing, they produce vast amounts of data. Some example application areas where sensor data is collected and processed include healthcare, environmental (including air quality and pollution levels), automotive, industrial, aerospace, and agricultural applications. These enormous volumes of sensing data collected from the edge devices are analyzed using a variety of Machine Learning (ML) and Deep Learning (DL) approaches. However, analyzing them on the cloud or a server presents challenges related to privacy, hardware, and connectivity limitations. Federated Learning (FL) is emerging as a solution to these problems while preserving privacy by jointly training a model without sharing raw data. In this paper, we review the FL strategies from the perspective of edge sensing devices to get over the limitations of conventional machine learning techniques. We focus on the key FL principles, software frameworks, and testbeds. We also explore the current sensor technologies, properties of the sensing devices and sensing applications where FL is utilized. We conclude with a discussion on open issues and future research directions on FL for further studies

翻译：环境特征监测、与之交互并获取周围环境信息的能力，得益于物联网、移动设备和可穿戴设备等边缘感知设备的快速普及，以及其集成传感器的测量能力。尽管这些设备体积小巧、数据存储和处理能力有限，却产生了海量数据。传感器数据的采集与处理应用领域包括医疗健康、环境监测（涵盖空气质量和污染水平）、汽车、工业、航空航天及农业等。从边缘设备收集的庞大数据集通过多种机器学习和深度学习方法进行分析。然而，在云端或服务器上分析这些数据面临隐私、硬件和连接性限制等挑战。联邦学习作为一种解决方案应运而生，它通过在不共享原始数据的情况下联合训练模型来保护隐私。本文从边缘感知设备视角综述了联邦学习策略，旨在克服传统机器学习方法的局限性。我们重点阐述了联邦学习的核心原理、软件框架及测试平台，并探讨了当前传感器技术、感知设备特性以及应用联邦学习的感知场景。最后，我们讨论了联邦学习待解决的开放性问题与未来研究方向。