Work

A Federated Framework for Air Quality Prediction in Smart Cities

Year: 2025

Type: article

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.

Source: International Journal of Innovations in Science and Technology

DOI: 10.33411/ijist/20257319711987

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

Institutions: University of Management and Technology, University of Engineering and Technology Lahore

Language: English

Topic: Air Quality Monitoring and Forecasting

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W7083288486

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