A Comparative Analysis of Centralized and Federated Learning for Multimodal ECG and PCG Classification

In this study, we analyzed federated learning (FL) for ECG and PCG data from the PhysioNet 2016 challenge dataset. We tested multiple approaches of FL and evaluated how these approaches affect the performance metrics of cardiac abnormality detection while preserving data privacy. We compared the performance of the centralized and federated models with two and four clients. The results demonstrated that multimodal federated models using both ECG and PCG data consistently outperformed centralized single-modality ECG or PCG models; in fact the gains provided by multimodal approaches can compensate for the loss in performance induced by distributed learning. These findings highlight the potential of multimodal federated learning to not only provide decentralization advantages but also to achieve comparable performance with the centralized single-modality approaches.Clinical relevance- The clinical relevance of this research lies in its potential to improve cardiovascular disease detection by exploring multimodal models and federated learning. It can also help to optimize machine learning models for real-world clinical deployment while preserving patient privacy and achieving comparable performance metrics.