FCL-IWQMS: Federated Continual Learning and IoT-Based Water Quality Monitoring System for Adaptive Real-Time Insights

In this paper, we propose a federated continual learning-based IoT system for real-time monitoring of surface water quality, named FCL-IWQMS. This system improves surface water monitoring management by integrating sensor networks and predictive analytics, addressing the challenges of climate change and urbanization. FCL-IWQMS enables collaboration by allowing Internet of Things (IoT) devices to send only updates from their local models to a central server, consolidating them to generate an improved prediction model. This approach ensures data privacy while enhancing the accuracy of water quality predictions. The framework utilizes methods such as Prioritized Experience Replay (PER), Learning Without Forgetting (LWF), and Elastic Weight Consolidation (EWC) to refine local models in response to new data. Local updates are periodically sent to aggregation nodes, where techniques like Federated Averaging (FedAvg), Federated Trimmed Mean (FedTM), and Agnostic Model Update (AMU) are applied to consolidate updates. Evaluations with 40 clients using publicly available datasets show that the FedAvg-PER model outperforms others in predicting dissolved oxygen (DO). At the same time, AMU-LWF excels in pH predictions, and FedTM-PER leads in electrical conductivity (EC).