Publication

Abstract : Ensuring clean water availability is critical for sustainability and health. Conventional water quality assessments are limited by manual sampling, poor temporal resolution, and centralized data processing. This study proposes HydroFedNet, a multisource water quality monitoring framework that uses Federated Learning (FL) to integrate diverse data sources, including LANDSAT satellite imagery, RGB pond images, and Internet of Things (IoT) sensor streams. The spatio-spectral transfer learning network (Spatio-Spectral TLNet), the color transfer learning network (Color TLNet) and the sensor convolutional neural network - temporal convolutional network (Sensor CNN - TCN) are fundamental models for HydroFedNet. Spatio-Spectral TLNet and Color TLNet leverage EfficientNetB3 for optimized, low-cost training, while Sensor CNN–TCN exploits improved temporal modeling. Models are trained locally and share weight updates with a central server, which builds a global model using the chosen FL strategy. FL strategies such as Federated Averaging (FedAvg), FL with Temporally Aware aggregation (FedLTA), and Federated Optimization (FedOpt) are evaluated with six objectives, including energy efficiency, fault tolerance, and handling of non-independent and identically distributed (non-IID) data. FedLTA surpasses the 90% accuracy across all three models with less communication overhead, whereas FedOpt effectively handles non-IID data. HydroFedNet allows an optimal selection of an intent-aware FL strategy, allowing robust, scalable, and efficient water quality monitoring across heterogeneous environments.