A Federated Framework for Air Quality Prediction in Smart Cities

Shahan  Uddin; Talha  Waheed; Hamid Raza  Malik; Abdul Basit  Dogar; Naeem A. Nawaz; Kashif Ishaq

Authors

Shahan Uddin School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
Talha Waheed Department of Computer Science, University of Engineering and Technology, Lahore, Pakistan
Hamid Raza Malik School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
Abdul Basit Dogar School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
Naeem A. Nawaz School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
Kashif Ishaq School of Systems and Technology, University of Management and Technology, Lahore, Pakistan

Keywords:

Air pollution detection, Federated Learning, Machine Learning algorithms, Urbanization and Industrialization impact, Health risks

Abstract

Over the last couple of decades, due to the constant increase in urbanization and industrialization, the concern in terms of air pollution has become a serious issue. In most cities, the pollution in the air is mostly comprised of Nitrogen Dioxide (NO₂), Ozone (O₃), Carbon Monoxide, and Particulate Matter, all of which can cause serious health issues. There is an emergent need for a system to detect air pollution. This research presents a framework that uses Federated Learning to lessen the communication overhead during the prediction process and ensure data privacy. The research also uses different Machine Learning algorithms, such as Random Forest, Support Vector Machine (SVM), and Logistic Regression, to train and evaluate the research.

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