Citation :

, 2026. "Federated Learning for Privacy-Preserving Medical Data Analytics" ESP International Journal of Artificial Intelligence & Data Science [IJAIDS]  Volume 1, Issue 1: 01-14.

Abstract :

The unprecedented digitization of present-day health care systems has resulted in an exponential growth in the quantity and variety of medical data, including electronic health records (EHRs), medical imaging, and output from wearable sensors and genomics dataset. These data sources offer a strong potential for enhancing clinical decision-making, personalized medicine, and predictive analytics. Yet the sensitive nature of medical information presents serious challenges stemming from patient privacy, data security, and compliance with rigorous regulatory regimes such as the Health Insurance Portability & Accountability Act (HIPAA) and General Data Protection Regulation (GDPR). Traditional centralized machine learning methods aggregate data in one location, creating vulnerabilities to security breaches, unauthorized access, and regulatory non-compliance.

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Keywords :

Federated Learning, Privacy-Preserving Analytics, Healthcare Data Security, Medical Imaging, Differential Privacy, Secure Aggregation,Holomorphicc Encryption, and Distributed Machine Learning— about 2 out of every headline.