"Encrypted federated learning for secure decentralized collaboration in ..."

Daniel Truhn et al. (2024)

> Home

Details and statistics

DOI: 10.1016/J.MEDIA.2023.103059

access: open

type: Journal Article

metadata version: 2024-03-08

- view
  - electronic edition via DOI (open access)
  authority control:
- export record
  dblp key:
  - journals/mia/TruhnASMKQWGHJLSBBYCHFHKSIBKKNK24
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/journals/mia/TruhnASMKQWGHJLSBBYCHFHKSIBKKNK24
Daniel Truhn, Soroosh Tayebi Arasteh, Oliver Lester Saldanha, Gustav Müller-Franzes, Firas Khader, Philip Quirke, Nicholas P. West, Richard Gray, Gordon G. A. Hutchins, Jacqueline A. James, Maurice B. Loughrey, Manuel Salto-Tellez, Hermann Brenner, Alexander Brobeil, Tanwei Yuan, Jenny Chang-Claude, Michael Hoffmeister, Sebastian Foersch, Tianyu Han, Sebastian Keil, Maximilian Schulze-Hagen, Peter Isfort, Philipp Bruners, Georgios Kaissis, Christiane Kuhl, Sven Nebelung, Jakob Nikolas Kather:
Encrypted federated learning for secure decentralized collaboration in cancer image analysis. Medical Image Anal. 92: 103059 (2024)

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.