Federated Learning for Privacy-Preserving AI in Mobile Health Applications

Authors- Vishwas M N

Abstract--The rapid growth of mobile health (mHealth) applications has led to a paradigm shift in how healthcare services are delivered, offering real-time monitoring, remote diagnostics, and personalized interventions. However, the increased reliance on personal health data raises significant concerns about user privacy, data ownership, and compliance with data protection regulations. Federated learning (FL), a decentralized machine learning approach, offers a promising solution by enabling collaborative model training across distributed devices without sharing raw data. This paper explores the application of federated learning in privacy-preserving AI for mHealth systems. It outlines the foundational principles of FL, its technological enablers, and the types of use cases where it is most impactful. Through real-world case studies and pilot deployments, the paper demonstrates how FL can enhance clinical decision-making, chronic disease management, and remote diagnostics while safeguarding sensitive user information. Ethical and regulatory considerations are examined, including consent mechanisms, transparency, and alignment with legal frameworks such as HIPAA and GDPR. The paper also discusses technical and operational challenges, including system heterogeneity, communication overhead, and model performance trade-offs. Future directions such as edge AI, differential privacy, and integration with wearable technologies are highlighted as emerging frontiers. This review underscores the transformative role of federated learning in delivering secure, scalable, and patient-centered mobile healthcare solutions.

DOI: /10.61463/ijset.vol.13.issue2.384