QFTN : A Quantum‐Inspired Federated Trust Negotiation Framework for Secure Data Exchange in IoRT

ABSTRACT The Internet of Robotic Things (IoRT) requires secure and trustworthy collaboration among distributed robotic agents, yet conventional federated learning approaches often overlook trust management and secure keying. This research proposes QFTN, a quantum‐inspired federated trust negotiation framework for resilient data exchange in IoRT. Each robot employs isolation forest‐based anomaly detection and computes a local trust score from validation performance. Trust values are aggregated through a federated protocol, while quantum‐inspired logistic‐map chaos functions generate pseudo‐random session keys for Fernet‐based symmetric encryption. Data exchange is permitted only when trust scores exceed a defined threshold, ensuring compromised nodes are excluded. Evaluations on the CIC‐IoT‐2023 dataset show that QFTN achieves strong detection accuracy (99.94%), precision (99.81%), recall (99.79%), F 1 score (99.80%), efficient trust convergence, and robust protection against adversarial exchanges, outperforming conventional federated baselines. The framework demonstrates that lightweight, trust‐aware, and quantum‐inspired mechanisms can effectively secure next‐generation IoRT ecosystems.