Guest Editorial Special Issue on Federated Learning for Medical Imaging: Enabling Collaborative Development of Robust AI Models

Federated Learning (FL) could solve the challenges of training AI models on large datasets for medical imaging due to data privacy and ownership concerns by allowing collaborative without need sharing raw data. This Special Issue Medical Imaging features papers covering FL-related topics discussing their implications healthcare imaging. The included articles focus a broad range federated scenarios applications, such as semi-supervised self-supervised learning, histopathology, image reconstruction, graph neural networks, preservation, active auditing, multi-task personalization, swarm learning. importance unbiased, privacy-preserving, generalizable that have potential be translated into clinical practice increases techniques FL. in this moved needle markedly forward regard.