Federated learning-based air quality prediction for smart cities using BGRU model

Abstract

Nowadays, Internet of Things (IoT) has become very popular due to its applications in various fields such as industry, commerce, and education. Cities become smart cities by utilizing lots of applications and services of IoT. However, these intelligent applications and services significantly threaten the environment regarding air pollution. Therefore, high accuracy in air pollution monitoring and future air quality predictions have become our primary concern to save human beings from health issues coming from air pollution. In general, deep learning (DL) and federated learning (FL) techniques are suitable for solving various forecasting problems and dealing with the high volatile air components in heterogeneous big data scenarios. This ambiance of DL and FL motivates us to exploit the DL-based Bidirectional Gated Recurrent Unit (BGRU) method for future air quality prediction using big data and federated learning (FL) to train our model in a distributed, decentral, and secure ways. This paper proposes a novel distributed and decentralized FL-based BGRU model to accurately predict air quality using the smart city's big data. The effectiveness of the FL-based BGRU Model is estimated with other machine learning (ML) models by using various evaluation metrics.