Federated Learning on Edge Sensing Devices: A Review

TL;DR

This review paper discusses federated learning applied to edge sensing devices, highlighting its principles, frameworks, applications, challenges, and future research directions to address privacy and resource limitations.

Contribution

It provides a comprehensive overview of federated learning strategies, sensor technologies, and applications on edge devices, emphasizing current challenges and future research avenues.

Findings

Federated learning enhances privacy in edge sensing applications.

Various software frameworks and testbeds support FL deployment on edge devices.

Open issues include resource constraints and data heterogeneity.

Abstract

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