Federated Learning for Healthcare: A Privacy-Preserving Approach to Medical Data Analysis
Abstract
With the increasing adoption of AI in healthcare, preserving patient privacy while leveraging large-scale medical data remains a significant challenge. This paper explores federated learning (FL) as a solution for decentralized AI training in healthcare applications. We propose an FL framework that enables multiple hospitals to collaboratively train machine learning models without sharing sensitive patient data. Our experiments on real-world medical datasets show that FL achieves performance comparable to centralized models while ensuring compliance with data privacy regulations such as HIPAA and GDPR. The study highlights the potential of FL in revolutionizing secure and ethical AI-driven healthcare solutions.
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