Federated Explainable AI Framework for Secure Healthcare Systems and Financial Enterprise Cloud Data Analytics

Abstract

The increasing adoption of artificial intelligence and cloud computing in healthcare and financial sectors has created significant opportunities for advanced data analytics and intelligent decision-making. However, these systems often rely on centralized data processing and complex machine learning models that raise concerns regarding data privacy, transparency, and regulatory compliance. Sensitive information such as patient health records and financial transactions must be protected while still enabling collaborative analytics across organizations. This research proposes a Federated Explainable Artificial Intelligence (FEAI) framework designed to support secure and transparent data analytics for healthcare systems and financial enterprise cloud environments. The framework integrates federated learning with explainable AI techniques to enable distributed model training without sharing raw data while maintaining interpretability of model predictions. By combining privacy-preserving distributed learning with transparent decision-making mechanisms, the proposed system ensures both data confidentiality and model accountability. The research methodology involves the design of a federated cloud architecture, implementation of machine learning models with explainability modules, and evaluation using simulated healthcare and financial datasets. Experimental analysis demonstrates that the framework provides strong privacy protection, accurate predictive analytics, and improved interpretability for decision support systems. The proposed approach contributes to the development of trustworthy AI-driven analytics platforms for sensitive enterprise environments.