Encrypted federated learning for secure decentralized collaboration in cancer image analysis

Authors:
raw_author_name: Daniel Truhn
raw_affiliation_string: Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany; Medical Faculty Heidelberg, Heidelberg University, Heidelberg
raw_author_name: Soroosh Tayebi Arasteh
raw_affiliation_string: Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
raw_author_name: Oliver Lester Saldanha
raw_affiliation_string: Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany; Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
raw_author_name: Gustav Müller-Franzes
raw_affiliation_string: Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
raw_author_name: Firas Khader
raw_affiliation_string: Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
raw_author_name: Philip Quirke
raw_affiliation_string: Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
raw_author_name: Nicholas P. West
raw_affiliation_string: Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
raw_author_name: Richard Gray
raw_affiliation_string: Clinical Trial Service Unit, University of Oxford, Oxford, United Kingdom
raw_author_name: Jacqueline A. James
raw_affiliation_string: Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast, United Kingdom; The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, United Kingdom
raw_author_name: Maurice B. Loughrey
raw_affiliation_string: Department of Cellular Pathology, Belfast Health and Social Care Trust, Belfast, United Kingdom; The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, United Kingdom
raw_author_name: Manuel Salto-Tellez
raw_affiliation_string: Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast, United Kingdom; The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, United Kingdom
raw_author_name: Hermann Brenner
raw_affiliation_string: Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
raw_author_name: Alexander Brobeil
raw_affiliation_string: Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany; Tissue Bank, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
raw_author_name: Tanwei Yuan
raw_affiliation_string: Cancer Epidemiology Group, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
raw_author_name: Jenny Chang-Claude
raw_affiliation_string: Cancer Epidemiology Group, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
raw_author_name: Michael Hoffmeister
raw_affiliation_string: Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
raw_author_name: Sebastian Foersch
raw_affiliation_string: Institute of Pathology, University Medical Center Mainz, Mainz, Germany
raw_author_name: Tianyu Han
raw_affiliation_string: Physics of Molecular Imaging Systems, Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
raw_author_name: Georgios Kaissis
raw_author_name: Christiane Kuhl
raw_author_name: Sven Nebelung
raw_author_name: Jakob Nikolas Kather

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.