Abstract

The increasing volume and complexity of healthcare data present both opportunities and challenges for predictive modeling. While big data analytics holds immense promise for improving diagnosis, treatment, and patient outcomes, the sensitive nature of medical information necessitates stringent privacy safeguards. This paper proposes a novel approach that combines Federated Learning (FL) with Differential Privacy (DP) to enable collaborative model training across multiple healthcare institutions without directly sharing patient data. We develop and evaluate a framework that allows for distributed model training while ensuring patient privacy through the application of DP mechanisms during the aggregation of model updates. Our results demonstrate that this approach can achieve comparable predictive performance to centralized training while significantly mitigating privacy risks. We analyze the trade-off between privacy and accuracy and provide insights into the optimal configuration of DP parameters for healthcare applications. The proposed framework offers a practical and scalable solution for leveraging the power of big data in healthcare while upholding ethical and legal obligations related to data privacy.