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Title:

Can Collaborative Learning Be Private, Robust and Scalable?

Document type:
Proceedings Paper
Author(s):
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...     »
Journal title abbreviation:
Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv
Year:
2022
Journal volume:
13573
Pages contribution:
37-46
Fulltext / DOI:
doi:10.1007/978-3-031-18523-6_4
Print-ISSN:
0302-9743
TUM Institution:
Institut für KI und Informatik in der Medizin (Prof. Rückert)
 BibTeX