Federated Learning for Air Quality Index Prediction: An Overview

Abstract

The air quality index forecast in big cities is an exciting study area in smart cities and healthcare on the Internet of Things. In recent years, a large number of empirical, academic, and review papers using machine learning for air quality analysis have been published. However, most of those studies focused on traditional centralized processing on a single machine, and there had been few surveys of federated learning in this field. This overview aims to fill this gap and provide newcomers with a broader perspective to inform future research on this topic, especially for the multi-model approach. We have examined over 70 carefully selected papers in this scope and discovered that multi-model federated learning is the most effective technique that could enhance the air quality index prediction result. Therefore, this mechanism needs to be considered by science community in the coming years.