Enhancing Federated Learning Through Differential Privacy: Introducing FedHybrid for Multicenter Diverse Heart Disease Datasets

Heart disease prediction using diverse, multicenter datasets poses challenges due to data heterogeneity, privacy concerns, and non-IID (Non-Independent and Identically Distributed) data. This paper introduces FedHybrid, a novel Federated Learning (FL) framework designed to address these issues by incorporating differential privacy via a Laplace mechanism, ensuring secure and optimized model aggregation and adaptive learning rate for faster convergence across IID (Independent and Identically Distributed) and non-IID data scenarios. It effectively handles eight diverse datasets with varying sample sizes, outperforming conventional FL methods like FedAvg and FedProx while preserving patient privacy. Results show that FedHybrid with differential privacy and adaptive learning rate mechanism achieves notable improvements in both convergence and accuracy. With 2 clients, FedHybrid reaches 88.24% accuracy in just 2 communication rounds, while FedAvg and FedProx require 8 and 4 rounds, respectively. For 5 clients, FedHybrid achieves 91.6% accuracy in 5 rounds, outperforming FedAvg (89.52%) and FedProx (89.8%), both of which take 10 rounds. As the number of clients increases, FedHybrid continues to excel, reaching 85.08% accuracy with 15 clients in 20 rounds, while FedAvg and FedProx take longer with lower accuracy. FedHybrid enables multicenter institutions to train models collaboratively, efficiently, and securely. The proposed approach significantly reduces communication overhead while maintaining high accuracy, making it a robust and scalable solution for federated learning in healthcare applications, particularly for heart disease prediction with clinical data.