Explainable Federated Learning Model for Early Diabetes Detection in Smart Healthcare

Abstract

Diabetes is one of the most prevalent chronic diseases worldwide and poses significant challenges to healthcare systems due to its increasing incidence and associated complications. Early detection of diabetes is essential for timely intervention, effective disease management, and improved patient outcomes. Traditional machine learning models for diabetes prediction often require centralized data collection, which raises concerns regarding patient privacy, data security, and regulatory compliance. Federated Learning (FL) has emerged as a promising distributed learning paradigm that enables collaborative model training across multiple healthcare institutions without sharing sensitive patient data. However, the lack of transparency and interpretability in federated models limits their adoption in clinical decision-making environments where explainability is crucial. This paper proposes an Explainable Federated Learning Model for Early Diabetes Detection in Smart Healthcare that integrates privacy-preserving federated learning with explainable artificial intelligence (XAI) techniques. The proposed framework enables multiple hospitals, clinics, and healthcare centers to collaboratively train a global diabetes prediction model while maintaining data confidentiality at local sites. Advanced machine learning algorithms are combined with explainability mechanisms such as feature importance analysis, SHAP (Shapley Additive Explanations), and interpretable decision support to provide transparent predictions and clinical insights. The framework incorporates secure aggregation, model optimization, and real-time health monitoring to enhance prediction accuracy and reliability. Experimental evaluation demonstrates that the proposed approach achieves high classification performance while preserving patient privacy and improving model interpretability. The integration of explainable federated learning supports trustworthy AI-driven healthcare systems by enabling clinicians to understand prediction outcomes and make informed decisions. The proposed model offers a scalable, secure, and transparent solution for early diabetes detection in smart healthcare environments.