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Complex-Valued Federated Learning With Differential Privacy and MRI Applications

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Julia Schnabel

Prof. Dr.

Core PI

Computational Imaging and AI in Medicine

Georgios Kaissis

Georgios Kaissis

Dr.

Principal Investigator

* Former Principal Investigator

Abstract

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, -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.

inproceedings RKZ+24

DeCaF @MICCAI 2024

5th Workshop on Distributed, Collaborative and Federated Learning at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention. Marrakesh, Morocco, Oct 06-10, 2024.

Authors

A. Riess • A. Ziller • S. Kolek • D. Rückert • J. A. Schnabel • G. Kaissis

Links

DOI

Research Areas

 A2 | Mathematical Foundations

 C1 | Medicine

BibTeXKey: RKZ+24

#p-kaissis #p-kutyniok #p-rueckert #p-schnabel
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