Toward Real-World Deployment of Federated Learning in Healthcare: A Comprehensive Review of Hybrid Models and Data Simulation Tools

This review synthesizes recent advancements in federated learning (FL) frameworks tailored for sensitive domains such as mental healthcare, medical imaging, and non-IID data simulation. Some past study presents a hybrid privacy-preserving FL model that integrates clustered federated learning (CFL) and quantum federated learning (QFL) to enhance accuracy and privacy in stress detection using wearable devices. Other studies introduce FedArtML, a novel tool for generating controlled non-IID datasets, offering quantifiable metrics like Jensen–Shannon and Hellinger distances to assess data heterogeneity. Furthermore, some of the latest paper proposes a transfer learning-based FL architecture for breast cancer classification using mammography images, combining feature extraction with federated averaging to ensure privacy and robust diagnostic accuracy. Collectively, these works address key challenges in FL, including client heterogeneity, data imbalance, privacy preservation, and system performance. This review highlights the complementary strengths of hybrid architectures, synthetic data partitioning, and transfer learning in advancing real-world applications of federated learning in healthcare.