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. Method... More >

This study introduces GenAI-FDT (GenFedTwin Resilience), a decentralized architecture designed to address the growing vulnerability of IoT-enabled smart infrastructures to climate change, particularly in the context of data silos, strict privacy regulations, and the scarcity of ground-truth data for rare catastrophic events such as floods in Maharashtra. Unlike traditional centralized digital twins, which struggle with data heterogeneity and excessive communication costs, the proposed framework integrates quantized diffusion-based Generative AI, semi-supervised federated learning (FedProx), and lightweight LSTM-CNN digital twins deployed on resource-constrained edge devices like Raspberry Pi... More >