Real world federated learning with a knowledge distilled transformer for cardiac CT imaging

Real world federated learning with a knowledge distilled transformer for cardiac CT imaging

Abstract Federated learning is a renowned technique for utilizing decentralized data while preserving privacy. However, real-world applications often face challenges like partially labeled datasets, where only a few locations have certain expert annotations, leaving large portions of unlabeled data...

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Main Authors: Malte Tölle, Philipp Garthe, Clemens Scherer, Jan Moritz Seliger, Andreas Leha, Nina Krüger, Stefan Simm, Simon Martin, Sebastian Eble, Halvar Kelm, Moritz Bednorz, Florian André, Peter Bannas, Gerhard Diller, Norbert Frey, Stefan Groß, Anja Hennemuth, Lars Kaderali, Alexander Meyer, Eike Nagel, Stefan Orwat, Moritz Seiffert, Tim Friede, Tim Seidler, Sandy Engelhardt
Format: Article
Language:English
Published: Nature Portfolio 2025-02-01
Series:npj Digital Medicine
Online Access:https://doi.org/10.1038/s41746-025-01434-3
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https://doi.org/10.1038/s41746-025-01434-3

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