Abstract
Federated learning (FL) plays a vital role in boosting both accuracy and privacy in the collaborative medical imaging field. The importance of privacy increases with the diverse security standards across nations and corporations, particularly in healthcare and global FL initiatives. Current research on privacy attacks in federated medical imaging focuses on sophisticated gradient inversion attacks that can reconstruct images from FL communications.
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Parampottupadam S, Floca R, BouniasDet al. Client security alone fails in federated learning: 2D and 3D attack insights. Proc MICCAI EMERGE. 2024.
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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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Parampottupadam, S. et al. (2025). Abstract: Client Security Alone Fails in Federated Learning. 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_64
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DOI: https://doi.org/10.1007/978-3-658-47422-5_64
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