Federated Learning on Edge Sensing Devices: A Review
Berrenur Saylam Özlem Durmaz İncel
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