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Titel:

Can Collaborative Learning Be Private, Robust and Scalable?

Dokumenttyp:
Proceedings Paper
Autor(en):
Usynin, Dmitrii; Klause, Helena; Paetzold, Johannes C.; Rueckert, Daniel; Kaissis, Georgios
Abstract:
In federated learning for medical image analysis, the safety of the learning protocol is paramount. Such settings can often be compromised by adversaries that target either the private data used by the federation or the integrity of the model itself. This requires the medical imaging community to develop mechanisms to train collaborative models that are private and robust against adversarial data. In response to these challenges, we propose a practical open-source framework to study the effectiv...     »
Zeitschriftentitel:
Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv
Jahr:
2022
Band / Volume:
13573
Seitenangaben Beitrag:
37-46
Volltext / DOI:
doi:10.1007/978-3-031-18523-6_4
Print-ISSN:
0302-9743
TUM Einrichtung:
Institut für KI und Informatik in der Medizin (Prof. Rückert)
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