Journal Article

DKFZ-2023-02741

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Encrypted federated learning for secure decentralized collaboration in cancer image analysis.

Truhn, D. ; Tayebi Arasteh, S. ; Saldanha, O. L. ; Müller-Franzes, G. ; Khader, F. ; Quirke, P. ; West, N. P. ; Gray, R. ; Hutchins, G. G. A. ; James, J. A. ; Loughrey, M. B. ; Salto-Tellez, M. ; Brenner, H.DKFZ* ; Brobeil, A. ; Yuan, T.DKFZ* ; Chang-Claude, J.DKFZ* ; Hoffmeister, M.DKFZ* ; Foersch, S. ; Han, T. ; Keil, S. ; Schulze-Hagen, M. ; Isfort, P. ; Bruners, P. ; Kaissis, G. ; Kuhl, C. ; Nebelung, S. ; Kather, J. N.

2023
Elsevier Science Amsterdam [u.a.]

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Please use a persistent id in citations: doi:10.1016/j.media.2023.103059

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.

Keyword(s): Artificial intelligence ; Federated learning ; Histopathology ; Homomorphic encryption ; Privacy-preserving deep learning ; Radiology

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Contributing Institute(s):

1. C070 Klinische Epidemiologie und Alternf. (C070)
2. Präventive Onkologie (C120)
3. DKTK HD zentral (HD01)
4. C020 Epidemiologie von Krebs (C020)

Research Program(s):

1. 313 - Krebsrisikofaktoren und Prävention (POF4-313) (POF4-313)

Appears in the scientific report 2023

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Database coverage:
; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 10 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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