breast cancer; convolutional neural networks; federated learning; homomorphic encryption; masses and microcalcifications detection; Breast Cancer; Convolutional neural network; Data confidentiality; Federated learning; Fully homomorphic encryption; Ho-momorphic encryptions; Homomorphic-encryptions; Innovative approaches; Mass detection; Microcalcification detection; Artificial Intelligence; Computer Science Applications; Decision Sciences (miscellaneous); Information Systems and Management; Safety, Risk, Reliability and Quality
Abstract :
[en] This study explores the convergence of Federated Learning (FL) and Fully Homomorphic Encryption (FHE) through an innovative approach applied to a confidential dataset composed of mammograms from Belgian medical records. Our goal is to clarify the feasibility and challenges associated with integrating FHE into the context of Federated Learning, with a particular focus on evaluating the memory constraints inherent in FHE when using sensitive medical data. The results highlight notable limitations in terms of memory usage, underscoring the need for ongoing research to optimize FHE in real-world applications. Despite these challenges, our research demonstrates that FHE maintains comparable performance in terms of Receiver Operating Characteristic (ROC) curves, affirming the robustness of our approach in secure machine learning applications, especially in sectors where data confidentiality, such as medical data management, is imperative. The conclusions not only shed light on the technical limitations of FHE but also emphasize its potential for practical applications. By combining Federated Learning with FHE, our model preserves data confidentiality while ensuring the security of exchanges between participants and the central server
Disciplines :
Computer science
Author, co-author :
Lessage, Xavier ; Université de Mons - UMONS > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle ; Cetic, Applied Research Centre, Charleroi, Belgium
Collier, Leandro; Cetic, Applied Research Centre, Charleroi, Belgium
Van Ouytsel, Charles-Henry Bertrand; UCLouvain, Faculty of Engineering, Louvain-la-Neuve, Belgium
Legay, Axel; Umons, Faculty of Engineering, Mons, Belgium
Mahmoudi, Saïd ; Université de Mons - UMONS > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle
Massonet, Philippe; Cetic, Applied Research Centre, Charleroi, Belgium
Language :
English
Title :
Secure federated learning applied to medical imaging with fully homomorphic encryption
Publication date :
2024
Event name :
2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC)
Event place :
Houston, Usa
Event date :
07-02-2024 => 09-02-2024
Audience :
International
Main work title :
2024 IEEE 3rd International Conference on AI in Cybersecurity, ICAIC 2024
Publisher :
Institute of Electrical and Electronics Engineers Inc.
F114 - Informatique, Logiciel et Intelligence artificielle
Research institute :
R300 - Institut de Recherche en Technologies de l'Information et Sciences de l'Informatique
Funding text :
This research was partially funded by the project AIDE and Infortech of UMONS. Special thanks to Linda Broughton of UCLouvain University for proofreading the English structure of this document and Dr Salvatore Murgo of the Belgian Helora hospital network for annotating mammographic abnormalities.
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