Federated Learning for Multi-Modal Health Data Integration: Enhancing Diagnostic Accuracy and Ensuring Data Privacy

The healthcare sector is experiencing a surge in diverse health data, encompassing medical imaging, electronic health records and live sensor readings from wearable technology. Integrating these multi-modal datasets holds immense potential for improving medical care by facilitating better diagnostic accuracy, customized therapeutic approaches, and more comprehensive understanding of how diseases evolve. However, centralizing this sensitive patient data across various institutions raises significant privacy concerns and raises complex issues around data stewardship and administrative oversight.Federated learning has surfaced as a potential approach to harness the wealth of available data while safeguarding patient privacy. Federated Learning (FL) facilitates a collaborative approach to model training among various healthcare institutions, allowing them to work together without needing to exchange their raw data. This research presents an innovative FL framework specifically designed to integrate multi-modal health data. Our method tackles the issues of data variability and model integration in federated environments, with the goal of improving diagnostic precision and personalizing treatment suggestions, all while maintaining the confidentiality of patient data.