AI-Enabled Federated Learning for Privacy-Preserving Mobile Health Analytics and Cloud-Based Enterprise Clinical Decision Intelligence
DOI:
https://doi.org/10.15662/IJEETR.2026.0801004Keywords:
Federated Learning, Privacy-Preserving Analytics, Mobile Health, Clinical Research, Enterprise Decision Intelligence, Healthcare Data SecurityAbstract
The rapid growth of mobile health (mHealth) technologies and digital clinical research platforms has generated vast volumes of sensitive health data. While these data offer unprecedented opportunities for advanced analytics and enterprise decision intelligence, concerns surrounding privacy, security, regulatory compliance, and data ownership significantly limit centralized data sharing. Federated and privacy-preserving analytics have emerged as promising solutions to address these challenges by enabling collaborative data analysis without direct data exchange. This paper explores the integration of federated learning and privacy-preserving techniques—such as differential privacy, secure multi-party computation, and homomorphic encryption—within mobile health and clinical research environments. It further examines how these approaches can support enterprise decision intelligence by enabling data-driven insights across distributed healthcare systems while maintaining patient confidentiality. The paper reviews existing literature, outlines a comprehensive research methodology, and evaluates the advantages and limitations of federated privacy-preserving frameworks. The findings suggest that combining federated analytics with enterprise decision intelligence can enhance clinical outcomes, improve operational efficiency, and support ethical and regulatory compliance. However, challenges related to system complexity, communication overhead, and model governance remain. This study contributes a structured understanding of how decentralized analytics can transform healthcare research and enterprise-level decision-making.
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