EcoPredict: Assessing Distributed Machine Learning Methods for Predicting Urban Emissions

The growing number of vehicles has led to increased emissions of polluting gases, necessitating accurate forecasting for effective mitigation strategies and sustainable urban development. Leveraging computational resources in vehicles, this study presents a framework, called EcoPredict, for predicting CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions in collaborative vehicular network environments. The framework implements three forms of learning methods—centralized, federated, and split—using urban sensor networks for data collection. Experiments carried out in realistic vehicular mobility scenarios demonstrate the framework’s robustness and efficiency in providing real-time emission predictions. Each learning architecture has its own advantages and limitations regarding performance, training time, latency, communication overhead, and data privacy. Therefore, this work aims to assess their performance to analyze their effectiveness in urban environments.