Abstract
Federated learning (FL) enables collaborative model training while keeping data locally. Currently, most FL studies in radiology are conducted in simulated environments due to numerous hurdles impeding its translation into practice. The few existing real-world FL initiatives rarely communicate specific measures taken to overcome these hurdles.
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Bujotzek MR, Akünal Ü, Denner S et al. Real-world federated learning in radiology: hurdles to overcome and benefits to gain. J Am Med Inform Assoc. 2024;32(1):193–205.
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© 2025 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Bujotzek, M. et al. (2025). Abstract: Real-world Federated Learning in Radiology. In: Palm, C., et al. Bildverarbeitung für die Medizin 2025. BVM 2025. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-47422-5_46
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DOI: https://doi.org/10.1007/978-3-658-47422-5_46
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