Quantitative Insights into Federated Learning Efficiency in Multi-Modal Medical Data Analysis: A Case Study on Diagnostic Accuracy

Abstract

In the era of big data, the medical field faces challenges in efficiently training machine learning models while preserving patient privacy. This study investigates the application of federated learning (FL) in multi-modal medical data analysis, focusing on its efficacy and diagnostic accuracy. We implemented a federated learning framework tailored for multi-modal datasets derived from diverse medical imaging sources. Our quantitative analysis reveals that the federated approach significantly enhances model performance, achieving a diagnostic accuracy improvement of 12% relative to conventional methods. Through cross-validation techniques and error rate evaluations, we uncover the strengths and limitations of federated learning in real-world medical applications. The findings underscore the potential of FL as a viable strategy for managing sensitive medical data while maintaining high-quality analytical outputs.