Federated Learning in IoT: a Survey from a Resource-Constrained Perspective

TL;DR

This survey reviews the challenges of deploying Federated Learning in resource-limited IoT environments, analyzing solutions at client and server levels, and proposing new evaluation metrics for resource-constrained devices.

Contribution

It provides a comprehensive categorization of solutions for resource constraints in IoT-FL and introduces new metrics for evaluating these solutions.

Findings

Identifies key challenges in resource-constrained IoT-FL deployment.

Categorizes solutions based on application location (client or server).

Proposes new evaluation metrics for resource-limited IoT devices.

Abstract

The IoT ecosystem is able to leverage vast amounts of data for intelligent decision-making. Federated Learning (FL), a decentralized machine learning technique, is widely used to collect and train machine learning models from a variety of distributed data sources. Both IoT and FL systems can be complementary and used together. However, the resource-constrained nature of IoT devices prevents the widescale deployment FL in the real world. This research paper presents a comprehensive survey of the challenges and solutions associated with implementing Federated Learning (FL) in resource-constrained Internet of Things (IoT) environments, viewed from 2 levels, client and server. We focus on solutions regarding limited client resources, presence of heterogeneous client data, server capacity, and high communication costs, and assess their effectiveness in various scenarios. Furthermore, we categorize the solutions based on the location of their application, i.e., the IoT client, and the FL server. In addition to a comprehensive review of existing research and potential future directions, this paper also presents new evaluation metrics that would allow researchers to evaluate their solutions on resource-constrained IoT devices.