Federated Learning for Healthcare: A Scalable AI Approach to Big Data Analytics

Abstract

The rapid expansion of healthcare data, driven by electronic health records (EHRs), wearable devices, and genomic sequencing, has created unprecedented opportunities for improving patient outcomes through big data analytics. However, the sensitive nature of healthcare data poses significant challenges for centralized data processing, particularly concerning privacy, security, and compliance with regulations such as GDPR and HIPAA. Federated learning (FL), a distributed machine learning paradigm, emerges as a promising solution by enabling collaborative model training without transferring raw data. This paper explores the application of federated learning in healthcare, emphasizing its scalability, privacypreserving capabilities, and potential for enhancing clinical decision-making. A novel FL framework tailored to healthcare big data analytics is proposed and evaluated using real-world datasets, demonstrating high performance in predictive modeling, anomaly detection, and personalized treatment recommendations. The findings underscore the transformative potential of federated learning in achieving scalable, secure, and efficient AI-driven healthcare analytics