Federated Learning for Privacy-Preserving Healthcare Analytics

Abstract

The increasing reliance on healthcare data analytics has raised significant concerns regarding data privacy and security. Traditional centralized machine learning approaches require data aggregation, which can expose sensitive patient information to potential breaches. This paper presents a Federated Learning-based framework for Privacy-Preserving Healthcare Analytics that enables collaborative model training without sharing raw data. The proposed system allows multiple healthcare institutions to train a global model by locally updating model parameters and sharing only encrypted updates with a central server. This approach ensures data privacy while maintaining high model performance. Advanced techniques such as secure aggregation and differential privacy are incorporated to further enhance security. The framework is evaluated on healthcare datasets, demonstrating that federated learning achieves comparable accuracy to centralized models while preserving data confidentiality. Additionally, integration with prior research in machine learning and healthcare prediction strengthens the robustness and adaptability of the system. The study highlights the potential of federated learning in enabling secure, scalable, and collaborative healthcare analytics, particularly in environments with strict data privacy regulations.