On the Performance of Federated Learning Algorithms for IoT

Abstract

Federated Learning (FL) is a state-of-the-art technique used to build machine learning (ML) models based on distributed data sets. It enables In-Edge AI, preserves data locality, protects user data, and allows ownership. These characteristics of FL make it a suitable choice for IoT networks due to its intrinsic distributed infrastructure. However, FL presents a few unique challenges; the most noteworthy is training over largely heterogeneous data samples on IoT devices. The heterogeneity of devices and models in the complex IoT networks greatly influences the FL training process and makes traditional FL unsuitable to be directly deployed, while many recent research works claim to mitigate the negative impact of heterogeneity in FL networks, unfortunately, the effectiveness of these proposed solutions has never been studied and quantified. In this study, we thoroughly analyze the impact of heterogeneity in FL and present an overview of the practical problems exerted by the system and statistical heterogeneity. We have extensively investigated state-of-the-art algorithms focusing on their practical use over IoT networks. We have also conducted a comparative analysis of the top available federated algorithms over a heterogeneous dynamic IoT network. Our analysis shows that the existing solutions fail to effectively mitigate the problem, thus highlighting the significance of incorporating both system and statistical heterogeneity in FL system design.