Privacy-preserving federated IoT intelligence for multimodal airway liberation decision support via offline reinforcement learning

Abstract Determining optimal timing for mechanical ventilation weaning remains a persistent clinical challenge, particularly in patients managed for upper airway pathologies—including post-operative head and neck surgery, laryngeal dysfunction, and obstructive airway conditions—where standard weaning criteria derived from general ICU populations may inadequately capture disease-specific physiological dynamics. We present an IoT-driven cyber-physical framework that continuously acquires and fuses four heterogeneous bedside data streams—ventilator waveform parameters, diaphragm ultrasound imaging features, arterial blood gas indices, and patient baseline profiles—within a gated recurrent architecture augmented by cross-modal attention. A Conservative Q-Learning agent learns a dynamic ventilation parameter adjustment policy from retrospective offline data, while a discrete-time survival model provides calibrated, uncertainty-aware individual weaning success probability estimates. To support privacy-preserving multi-center deployment across otolaryngology and head and neck surgery units, the framework employs a FedProx federated learning protocol with Gaussian differential privacy ($$\\varepsilon = 1.0$$ ε=1.0) and MAML-style local personalization. Evaluated on 36,181 weaning episodes from MIMIC-IV and eICU-CRD, the system achieves an AUROC of 0.893 on internal validation and 0.871 on external validation, with the federated variant narrowing the gap to centralized training to within 0.001 AUROC. These results demonstrate that integrating IoT-scale multimodal sensing with offline reinforcement learning and federated optimization yields a generalizable decision support system applicable to airway-complex patient populations encountered in otolaryngological practice. Graphic Abstract