Water Quality Management using Federated Deep Learning in Developing Southeastern Asian Country

Machine learning (ML) has become a key technology for addressing water quality issues. In this study, we present the application of machine learning for real-time water quality management in a remote village located in Sindh, Pakistan. We conducted two experiments using IoT infrastructure. The first experiment utilized traditional ML models for data analysis, while the second employed the proposed Federated Deep Learning (FDL) framework. The aim was to compare the performance and efficiency of the two approaches in handling data from the IoT sensors. The IoT system is composed of a sensor network that monitors water quality parameters, including pH, temperature, Total Dissolved Solids (TDS), and turbidity. This paper demonstrates the application of ML for real-time water quality management in a remote village in Sindh, Pakistan, through two experiments leveraging the IoT infrastructure. Traditional ML models, including SVM, achieved accuracies of 91% and 93%, respectively, but they faced scalability issues. The proposed FDL framework outperformed these models, achieving 98% accuracy, 97% precision, and 96.01% recall. Furthermore, FDL preserves data privacy by local processing and improves scalability and computational efficiency. This decentralized approach enhances the reliability and efficiency, making it suitable for water monitoring in resource-limited settings.