Privacy preserving federated machine learning and block chaining for reduced cyber risks in a world of distributed healthcare

The FeatureCloud Vision The digital revolution, in particular big data and artificial intelligence (AI) offer new opportunities to transform healthcare. However, it also harbors risks to the safety of sensitive clinical data stored in critical healthcare ICT infrastructure. In particular, data exchange over the internet is perceived insurmountable, posing a roadblock hampering big data based medical innovations.

With FeatureCloud, we are involved in a pan-European transformative AI development project, which implements a software toolkit for substantially reducing cyber risks to healthcare infrastructure by employing the world-wide first privacy-by-architecture approach, which has two key characteristics: (1) no sensitive data is sent through any communication channels, and (2) data is not stored in one central point of attack.

We are in particular responsible for the creation of federated unsupervised methods, dealing with the privacy-aware federated preprocessing and clustering.

More Information can be found on the project website: https://featurecloud.eu


(Hartebrodt & Röttger, 2022) (Hartebrodt et al., 2021) (Torkzadehmahani et al., 2020) (Matschinske et al., 2021) (Matschinske et al., 2021)
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  2. Anne Hartebrodt, Reza Nasirigerdeh, David B Blumenthal and Richard Röttger. Federated Principal Component Analysis for Genome-Wide Association Studies. In 2021 IEEE International Conference on Data Mining (ICDM). (2021): 1090–1095. Link.
  3. Reihaneh Torkzadehmahani, Reza Nasirigerdeh, David B Blumenthal, Tim Kacprowski, Markus List, Julian Matschinske, Julian Späth, Nina Kerstin Wenke, Béla Bihari, Tobias Frisch, Anne Hartebrodt, Anne-Christin Hausschild, Dominik Heider, Andreas Holzinger, Walter Hötzendorfer, Markus Kastelitz, Rudolf Mayer, Cristian Nogales, Anastasia Pustozerova, Richard Röttger, Harald H.H.W. Schmidt, Ameli Schwalber, Christof Tschohl, Andrea Wohner and Jan Baumbach. Privacy-preserving Artificial Intelligence Techniques in Biomedicine. arXiv preprint arXiv:2007.11621 (2020). Link.
  4. Julian Matschinske, Julian Späth, Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Anne Hartebrodt, Balázs Orbán, Sándor Fejér, Olga Zolotareva, Mohammad Bakhtiari, Béla Bihari, Marcus Bloice, Nina C Donner, Walid Fdhila, Tobias Frisch, Anne-Christin Hauschild, Dominik Heider, Andreas Holzinger, Walter Hötzendorfer, Jan Hospes, Tim Kacprowski, Markus Kastelitz, Markus List, Rudolf Mayer, Mónika Moga, Heimo Müller, Anastasia Pustozerova, Richard Röttger, Anna Saranti, Harald HHW Schmidt, Christof Tschohl, Nina K Wenke and Jan Baumbach. The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond. arXiv preprint arXiv:2105.05734 (2021). Link.
  5. Julian Matschinske, Nicolas Alcaraz, Arriel Benis, Martin Golebiewski, Dominik G Grimm, Lukas Heumos, Tim Kacprowski, Olga Lazareva, Markus List, Zakaria Louadi, Josch K Pauling, Nico Pfeifer, Richard Röttger, Veit Schwämmle, Gregor Sturm, Alberto Traverso, Kristel Van Steen, Martiela Vaz Freitas, Gerda Cristal Villalba Silva, Leonard Wee, Nina K Wenke, Massimiliano Zanin, Olga Zolotareva, Jan Baumbach and David B Blumenthal. The AIMe registry for artificial intelligence in biomedical research. Nature methods 18(10): 1128–1131 (2021). Link.