Enhancing Collaborative Medical Image Diagnosis Using Federated Learning: A Case Study from Shenzhen’s Top Hospitals

Federated Learning (FL) represents a transformative approach in collaborative medical image diagnosis by enabling the training of high-quality AI models while preserving patient data privacy. This study explores the integration of FL in healthcare, focusing on data governance, system performance, and challenges such as statistical heterogeneity, communication overhead, and ethical considerations. Key findings reveal FL's potential to improve diagnostic accuracy across institutions, though issues like resource consumption and privacy vulnerabilities require further attention. Recommendations emphasize optimizing model architectures, standardizing evaluation metrics, and fostering international collaborations to enhance model generalizability and clinical applicability. Future research opportunities include integrating multimodal data and addressing convergence dynamics in FL algorithms. By tackling these challenges, FL has the potential to revolutionize medical diagnostics, ensuring both innovation and ethical compliance.