A Federated Framework for Air Quality Prediction in Smart Cities

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.