Quantum Enhanced Federated Edge Intelligence for Cyber Resilience in IoT Remote Sensing

Data quality and privacy have emerged as an area of special interest with the rapid proliferation of the Internet of Things (IoT) and edge computing in complex remote sensing. An efficient threat/anomaly detection mechanism requires secure operation in a real-time, distributed agricultural architecture. This research presents an integrated approach leveraging federated learning (FL) and quantum-inspired algorithms (QiA). The model proposes an edge-enabled federated quantum learning approach, “Q-EDGE,” across three locations that utilizes the FL scalability and QAs computational power. FL leverages QA to ensure data privacy and accelerate model convergence, enabling collaborative threat detection across distributed edge nodes. Results reveal that the proposed framework achieved high detection accuracy of up to 90% across three locations for various attack types. The overall performance of the Q-EDGE framework indicates strong learning dynamics and good model generalization. The integration of emerging quantum technologies with edge AI demonstrates the effectiveness of distributed cyber resilience in IoT remote-sensing infrastructures.