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@ARTICLE{Truhn:286250,
      author       = {D. Truhn and S. Tayebi Arasteh and O. L. Saldanha and G.
                      Müller-Franzes and F. Khader and P. Quirke and N. P. West
                      and R. Gray and G. G. A. Hutchins and J. A. James and M. B.
                      Loughrey and M. Salto-Tellez and H. Brenner$^*$ and A.
                      Brobeil and T. Yuan$^*$ and J. Chang-Claude$^*$ and M.
                      Hoffmeister$^*$ and S. Foersch and T. Han and S. Keil and M.
                      Schulze-Hagen and P. Isfort and P. Bruners and G. Kaissis
                      and C. Kuhl and S. Nebelung and J. N. Kather},
      title        = {{E}ncrypted federated learning for secure decentralized
                      collaboration in cancer image analysis.},
      journal      = {Medical image analysis},
      volume       = {92},
      issn         = {1361-8415},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DKFZ-2023-02741},
      pages        = {103059},
      year         = {2023},
      abstract     = {Artificial intelligence (AI) has a multitude of
                      applications in cancer research and oncology. However, the
                      training of AI systems is impeded by the limited
                      availability of large datasets due to data protection
                      requirements and other regulatory obstacles. Federated and
                      swarm learning represent possible solutions to this problem
                      by collaboratively training AI models while avoiding data
                      transfer. However, in these decentralized methods, weight
                      updates are still transferred to the aggregation server for
                      merging the models. This leaves the possibility for a breach
                      of data privacy, for example by model inversion or
                      membership inference attacks by untrusted servers.
                      Somewhat-homomorphically-encrypted federated learning
                      (SHEFL) is a solution to this problem because only encrypted
                      weights are transferred, and model updates are performed in
                      the encrypted space. Here, we demonstrate the first
                      successful implementation of SHEFL in a range of clinically
                      relevant tasks in cancer image analysis on multicentric
                      datasets in radiology and histopathology. We show that SHEFL
                      enables the training of AI models which outperform locally
                      trained models and perform on par with models which are
                      centrally trained. In the future, SHEFL can enable multiple
                      institutions to co-train AI models without forsaking data
                      governance and without ever transmitting any decryptable
                      data to untrusted servers.},
      keywords     = {Artificial intelligence (Other) / Federated learning
                      (Other) / Histopathology (Other) / Homomorphic encryption
                      (Other) / Privacy-preserving deep learning (Other) /
                      Radiology (Other)},
      cin          = {C070 / C120 / HD01 / C020},
      ddc          = {610},
      cid          = {I:(DE-He78)C070-20160331 / I:(DE-He78)C120-20160331 /
                      I:(DE-He78)HD01-20160331 / I:(DE-He78)C020-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38104402},
      doi          = {10.1016/j.media.2023.103059},
      url          = {https://inrepo02.dkfz.de/record/286250},
}