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

通过对边缘传感设备的视角，本文回顾了联邦学习策略，探讨了关键联邦学习原理、软件框架和测试平台，并且研究了联邦学习在当前传感技术、传感设备性能和传感应用上的应用，最后讨论了未来研究方向。