Explainable Multi-Modal Fusion-Based Federated Learning for Mortality Prediction in Energy-Constrained Healthcare Systems

The growing demand for privacy-preserving and reliable AI solutions in healthcare has made Federated Learning (FL) a captivating approach for clinical decision support systems. However, contempt its promise, many FL approaches struggle to balance predictive accuracy with explainability, an essential requirement for clinical approval. To address this gap, this study presents an explainable multi-modality FL framework that consolidates a transformer model for unstructured clinical notes with a tree-based model for structured patient data. Using both data modalities, the proposed hybrid architecture empowers robust mortality prediction while ensuring traceability in model decision-making. The framework is evaluated in the large-scale MIMIC-IV dataset within a decentralized hospital environment, where the global fusion-based model achieves an AUC of 0.89 that surpasses traditional baselines while providing interpretable insights on clinical outcomes. These findings validate a scalable and reliable solution for the deployment of explainable AI (XAI) in federated healthcare systems, spanning the critical gap between model performance and clinical confidence.