Explainable Federated Learning Framework for Privacy-Preserving Healthcare Analytics

Abstract

The increasing adoption of artificial intelligence in healthcare has created unprecedented opportunities for disease diagnosis, patient risk prediction, medical image analysis, and clinical decision support. However, centralized machine learning approaches require healthcare institutions to share sensitive patient data, raising significant concerns regarding privacy, regulatory compliance, and data governance. Federated Learning (FL) has emerged as an effective distributed learning paradigm that enables multiple healthcare organizations to collaboratively train machine learning models without exchanging raw patient data. Although FL significantly enhances privacy preservation, the resulting models often remain difficult for clinicians to interpret, limiting their adoption in safety-critical healthcare environments. Consequently, integrating Explainable Artificial Intelligence (XAI) with Federated Learning has become an important research direction for developing trustworthy, transparent, and privacy-preserving healthcare analytics. Between 2018 and 2025, substantial advances were achieved in federated optimization algorithms, secure aggregation, differential privacy, homomorphic encryption, explainability techniques such as SHAP, LIME, Grad-CAM, and attention-based interpretation methods, as well as healthcare-oriented federated learning applications involving medical imaging, electronic health records, disease prediction, and clinical decision support. These developments have significantly improved collaborative model training while increasing transparency and clinician trust in AI-assisted healthcare systems.