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

Abstract

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

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