TY  - JOUR
AU  - Achary, K. Radha
AU  - Samal, Ajit Kumar
PY  - 2026
DA  - 2026/10/26
TI  - GenFedEdgeAgri: Diffusion-Enhanced Federated Edge AIoT for Climate-Resilient Precision Agriculture
JO  - Next-Generation Computing Systems and Technologies
T2  - Next-Generation Computing Systems and Technologies
JF  - Next-Generation Computing Systems and Technologies
VL  - 2
IS  - 4
SP  - 123
EP  - 130
DO  - 10.62762/NGCST.2026.661752
UR  - https://www.icck.org/article/abs/NGCST.2026.661752
KW  - generative diffusion models (GDM)
KW  - federated learning (FL)
KW  - edge AIoT
KW  - climate-resilient agriculture
KW  - synthetic data augmentation
KW  - smart irrigation
AB  - 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.
SN  - 3070-3328
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Achary2026GenFedEdge,
  author = {K. Radha Achary and Ajit Kumar Samal},
  title = {GenFedEdgeAgri: Diffusion-Enhanced Federated Edge AIoT for Climate-Resilient Precision Agriculture},
  journal = {Next-Generation Computing Systems and Technologies},
  year = {2026},
  volume = {2},
  number = {4},
  pages = {123-130},
  doi = {10.62762/NGCST.2026.661752},
  url = {https://www.icck.org/article/abs/NGCST.2026.661752},
  abstract = {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.},
  keywords = {generative diffusion models (GDM), federated learning (FL), edge AIoT, climate-resilient agriculture, synthetic data augmentation, smart irrigation},
  issn = {3070-3328},
  publisher = {Institute of Central Computation and Knowledge}
}