GenFedEdgeAgri: Diffusion-Enhanced Federated Edge AIoT for Climate-Resilient Precision Agriculture

Modern precision agriculture is undermined by data scarcity, privacy concerns, and high latency in remote areas, and centralized AI models struggle to forecast rare, climate-induced crop stresses given insufficiently diverse datasets and stakeholders' reluctance to share sensitive data. To address these obstacles, this study presents GenFedEdgeAgri, a framework aimed at improving climate-resilient crop monitoring, soil intelligence, and intelligent irrigation, and it examines how synthetic, data-driven augmentation-achieved by incorporating Generative Diffusion Models (GDMs) into a Federated Edge AIoT architecture-can enhance model robustness while preserving strong localized privacy. Methodologically, we propose a decentralized architecture in which Edge AIoT gateways employ lightweight Latent Diffusion Models to produce high-fidelity synthetic samples of nutrient depletion and extreme-weather stress; these samples harden local models before Federated Learning (FL) weight aggregation, thereby mitigating the effects of non-IID data distribution. We specify a pre-registered evaluation protocol-with defined datasets, baselines and metrics-under which the framework is compared against standard FL frameworks on multi-modal data in terms of accuracy, decision latency, and communication cost. The framework is evaluated against a pre-specified protocol; the projected gains-a 14.2% improvement in crop-disease detection accuracy, a 19% relative reduction in soil-moisture forecasting RMSE, and up to a 43% reduction in communication rounds-are stated as pre-registered hypotheses to be empirically confirmed, not as validated results. If confirmed, GenFedEdgeAgri would offer a scalable, privacy-preserving path to sustainable smart infrastructure, advancing autonomous, water-efficient agriculture through on-device intelligence that anticipates climate variability.