Hybrid Federated Authentication and AI-Driven Big Data Analytics for Secure and Intelligent Smart Farming

The rapid adoption of Internet of Things (IoT) in smart farming has significantly transformed agricultural operations, optimizing productivity, resource utilization, and real-time monitoring. However, key challenges remain, including secure authentication of distributed IoT devices, interoperability across farms, and efficient processing of heterogeneous agricultural data. To address these challenges, we propose a novel Hybrid Federated Authentication and AI-Driven Big Data Analytics (HFA-AIBA) model, which integrates a hybrid metaheuristicdeep learning (DL) algorithm for authentication and an AIpowered knowledge-based big data processing framework for real-time farm analytics. The authentication mechanism combines federated OpenID Connect (OIDC) with a hybrid swarm intelligence optimization (SIO) and deep reinforcement learning (DRL) model to enhance adaptive security against cyber threats. Additionally, the big data processing layer utilizes a hybrid deep neuro-evolutionary model with periodic self-learning updates, enabling anomaly detection in farm conditions, predictive crop health assessments, and intelligent decision-making. Our proposed HFA-AIBA framework ensures secure, scalable, and intelligent farm operations, improving authentication accuracy, optimizing big data insights, and fostering sustainable precision agriculture. Experimental validation demonstrates superior performance in authentication efficiency, anomaly detection precision, and predictive accuracy compared to existing smart farming architectures.