Privacy-Preserving Federated Learning for Collaborative Healthcare Analytics

Abstract

Abstract:

In the ever-evolving landscape of healthcare analytics, the need for collaborative research and data-driven decision-making is paramount. However, ensuring the privacy and security of sensitive patient information is a fundamental concern. This paper explores the application of Privacy-Preserving Federated Learning (PPFL) as a solution to bridge the gap between collaborative healthcare analytics and data privacy.

Federated learning enables multiple healthcare institutions to collaboratively train machine learning models without sharing raw patient data. Our research delves into the intricacies of implementing PPFL techniques, including secure aggregation protocols, cryptographic privacy-preserving mechanisms, and differential privacy measures.

We discuss the advantages of PPFL, such as preserving data locality, reducing data transmission, and empowering individual institutions to maintain control over their data. Furthermore, we address the challenges and trade-offs in PPFL adoption, including computational overhead and model performance.

To validate the effectiveness of PPFL in healthcare analytics, we present a case study involving multiple hospitals sharing clinical data for predictive modeling of disease outbreaks. Our results demonstrate that PPFL not only safeguards patient privacy but also yields accurate and robust models.