PRIVACY-AWARE EXPLAINABLE FEDERATED DEEP LEARNING FOR INTELLIGENT HEALTHCARE ANALYTICS

Abstract

The rapid digitization of healthcare data has enabled the development of intelligent analytics systems for disease prediction and clinical decision support. However, centralized deep learning approaches raise serious concerns regarding patient privacy, data security, and regulatory compliance. To address these challenges, this paper proposes a Privacy-Aware Explainable Federated Deep Learning (PAEFDL) framework for intelligent healthcare analytics. The proposed model enables multiple healthcare institutions to collaboratively train a global deep learning model without sharing raw patient data, thereby preserving data confidentiality. Differential privacy mechanisms and secure aggregation protocols are integrated to prevent information leakage during model updates. Additionally, explainable artificial intelligence (XAI) techniques such as SHAP and attention-based visualization are incorporated to enhance model transparency and clinical interpretability. Experimental evaluation on benchmark medical datasets demonstrates that the proposed framework achieves competitive predictive performance while ensuring strong privacy guarantees and improved model interpretability. The results highlight the feasibility of deploying secure, trustworthy, and collaborative AI systems in real-world healthcare environments.