Title: MedAlign: Clinician-Centered Federated Meta-Learning for Medical IoT with Privacy and Interpretability Guarantees
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Federated Learning, Representation Learning, Medical IoT, Edge Intelligence, Resource-Aware Optimization, Adaptive Gating, Differential Privacy, Multimodal Fusion

Abstract: We propose a resource-aware federated meta-learning framework for medical Internet of Things (IoT) scenarios characterized by pronounced data heterogeneity, strict privacy demands, and constrained device resources. MedAlign enables collaborative model optimization across distributed edge nodes, while supporting personalized adaptation to diverse clinical environments. A lightweight adaptive gating controller dynamically orchestrates module activation in response to real-time computational, energy, and latency constraints, ensuring efficient inference and update on heterogeneous hardware. The architecture integrates ontology-driven feature selection, multimodal fusion of physiological and network signals, and prototype-consistent representation learning to maintain robust diagnostic boundaries across institutions. Privacy is rigorously protected via a formally calibrated aggregation protocol that injects controlled noise, providing quantifiable confidentiality guarantees without degrading clinical utility. Extensive experiments on intensive care and wearable health datasets demonstrate consistent improvements in detection accuracy, training efficiency, and resource utilization compared to existing approaches.

TL;DR: MedAlign is a privacy-preserving federated meta-learning framework that improves interpretability and efficiency for clinician-centered AI in medical IoT.