TL;DR
The results indicate that adaptive multimodal federated modeling, along with systematic preprocessing and interpretable decision support, offers a scalable and therapeutically feasible approach for distributed cardiovascular risk stratification.
A PFL framework, FedSCF, which models client heterogeneity at the parameter level, including a relative perturbation-based sensitivity evaluation is designed to identify critical parameters for personalized modeling, while the remaining parameters participate in cross-client sharing.
The proposed FSSL framework provides a scalable foundation for privacy-conscious collaborative clinical AI while keeping patient data within the originating healthcare institution and is intended to support, rather than replace, professional clinical decision-making.
Artificial intelligence (AI)-powered heart sound auscultation offers a noninvasive and accessible approach for diagnosing cardiovascular diseases (CVDs). However, training robust diagnostic models requires large-scale data, which is hindered by strict privacy regulations across isolated medical institutions. While fede...
The proposed FL framework provides a privacy-preserving, explainable, and computationally efficient solution for collaborative AI in medical imaging by combining adaptive federated learning, secure privacy mechanisms, and explainable AI techniques, demonstrating strong potential for deployment in multi-hospital clinica...
The Feature-Augmented Analytic Federated Architecture robustly guarantees generalization stability, substantially outperforms existing paradigms in predictive fidelity and computational efficiency, and establishes a new operational standard for mission-critical clinical networks.
A novel multi-modal cloud architecture is proposed, which combines edge computing, cloud data lakes, multimodal feature fusion, explainable artificial intelligence (XAI) and federated learning technologies to achieve secure and scalable predictive clinical analytics.
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