Federated learning on magnetic resonance imaging: a critical review

Abstract Federated learning is a promising method for developing collaborative and privacy-preserving medical AI models in dispersed geographical areas. While FL enables the development of robust and generalizable imaging models by leveraging diverse data sources distributed across different institutions, it has not yet been fully standardized, and numerous open issues remain to be resolved. These include interoperability, security vulnerabilities, optimal strategies for model aggregation, and privacy safeguarding. In this critical review, a thorough survey of FL methods on MRI image analysis has been performed. In particular, parameters such as the models used, datasets, harmonization techniques, aggregation methods, security and privacy measures, “real-world” implementations, and performance measurements were investigated. This review sheds light, from a critical perspective, on the required infrastructure of developing deep federated learning applications based on MRI data such as lesion detection, classification, image reconstruction, and tumor segmentation. Unlike previous FL reviews, this work’s main focus is MRI since its tomographic data structure, acquisition protocol variability, and inter-scanner heterogeneity introduce challenges not typically present in modalities such as ultrasound, X-ray, or computed tomography. At the same time, the wide use of MRI in clinical routine makes it an excellent fit for FL architectures that leverage diverse and heterogeneous data sources to develop generalized models while safeguarding patient confidentiality. The present review addresses an important gap in the literature by offering domain-specific insights needed to advance the development of reliable MRI-based FL systems.