SMARTWATERSHED: FEDERATED LEARNING FOR DECENTRALIZED WATER QUALITY PREDICTION IN AGRICULTURAL ZONES

Water quality management in agricultural watersheds is critical for sustainable farming and environmental conservation, yet traditional centralized models face challenges in data privacy, scalability, and latency.We introduce SmartWatershed, a novel federated learning (FL) framework designed for decentralized water quality prediction in agricultural zones.By leveraging edge devices (e.g., IoT sensors and farmer-owned gateways), SmartWatershed enables privacy-preserving, real-time predictions of key water quality parameters, including nitrate levels, pH, temperature, and dissolved oxygen.Our unique contribution, Adaptive Gradient Aggregation with Temporal Weighting (AGA-TW), dynamically prioritizes client updates based on data freshness and environmental variability, addressing non-i.i.d.data challenges common in agricultural settings.This mechanism enhances model robustness by weighting contributions from devices experiencing dynamic conditions, such as seasonal runoff or pollution events, while reducing communication overhead through quantization and threshold-based filtering.Experiments on a synthetic dataset inspired by real-world watersheds (10,000 samples, 50 devices) demonstrate that SmartWatershed achieves a Sangeethavani B https://iaeme.com/Home/