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Riess, A. ; Ziller, A.* ; Kolek, S.* ; Rueckert, D.* ; Schnabel, J.A. ; Kaissis, G.

Complex-valued federated learning with differential privacy and MRI applications.

In: (Medical Image Computing and Computer Assisted Intervention – MICCAI 2024). Berlin [u.a.]: Springer, 2025. 191-203 (Lect. Notes Comput. Sc. ; 15274 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Federated learning enhanced with Differential Privacy (DP) is a powerful privacy-preserving strategy to protect individuals sharing their sensitive data for processing in fields such as medicine and healthcare. Many medical applications, for example magnetic resonance imaging (MRI), rely on complex-valued signal processing techniques for data acquisition and analysis. However, the appropriate application of DP to complex-valued data is still underexplored. To address this issue, from the theoretical side, we introduce the complex-valued Gaussian mechanism, whose behaviour we characterise in terms of f-DP, (ε,δ)-DP and Rényi-DP. Moreover, we generalise the fundamental algorithm DP stochastic gradient descent to complex-valued neural networks and present novel complex-valued neural network primitives compatible with DP. Experimentally, we showcase a proof-of-concept by training federated complex-valued neural networks with DP on a real-world task (MRI pulse sequence classification in k-space), yielding excellent utility and privacy. Our results highlight the relevance of combining federated learning with robust privacy-preserving techniques in the MRI context.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Complex Numbers ; Differential Privacy ; Federated Learning
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Medical Image Computing and Computer Assisted Intervention – MICCAI 2024
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15274 LNCS, Heft: , Seiten: 191-203 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
Förderungen German Bundestag, under the frame of ERA PerMed
German Federal Ministry of Health
German Academic Exchange Service (DAAD) under the Kondrad Zuse School of Excellence for Reliable AI (RelAI)
Medical Informatics Initiative as part of the PrivateAIM Project, from the Bavarian Collaborative Research Project PRIPREKI of the Free State of Bavaria Funding Programme "Artificial Intelligence - Data Science"
German Ministry of Education and Research
Bavarian State Ministry for Science and the Arts under the Munich Centre for Machine Learning
German Federal Ministry of Education and Research