Federated Learning for Privacy Preservation in Healthcare Systems

Abstract

Background: The increasing digitalization of healthcare has led to unprecedented volumes of sensitive patient data, raising significant concerns about privacy and data security. Federated learning (FL) offers a promising solution by enabling collaborative model training without sharing raw data among institutions. Methods: This study evaluates FL as a privacy-preserving methodology in healthcare by comparing its predictive performance to centralized models. A simulated multi-institutional environment was established using heterogeneous datasets, including electronic health records and medical imaging data. Differential privacy techniques were integrated to safeguard data during the training process. Performance metrics such as accuracy, precision, recall, and AUC-ROC were computed, while privacy was quantified using the epsilon parameter. Communication overhead and scalability were also assessed. Results: The FL framework achieved predictive metrics comparable to centralized approaches, with only marginal differences (accuracy of 92.3% vs. 93.0%). The convergence analysis confirmed stable model training, and the controlled increase in epsilon values demonstrated robust privacy preservation. Communication overhead increased linearly with the number of institutions, yet remained within manageable limits, indicating effective scalability. Conclusion: Federated learning proves to be a viable and secure alternative for healthcare analytics, balancing high predictive performance with enhanced privacy protection. Future research should focus on addressing data heterogeneity and optimizing communication protocols to further strengthen the application of FL in real-world clinical settings.