FedViTBloc: Secure and privacy-enhanced medical image analysis with federated vision transformer and blockchain
The increasing prevalence of cancer necessitates advanced methodologies for early detection and diagnosis. Early intervention is crucial for improving patient outcomes and reducing the overall burden on healthcare systems. Traditional centralized methods of medical image analysis pose significant ri...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | High-Confidence Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667295225000066 |
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| author | Gabriel Chukwunonso Amaizu Akshita Maradapu Vera Venkata Sai Sanjay Bhardwaj Dong-Seong Kim Madhuri Siddula Yingshu Li |
| author_facet | Gabriel Chukwunonso Amaizu Akshita Maradapu Vera Venkata Sai Sanjay Bhardwaj Dong-Seong Kim Madhuri Siddula Yingshu Li |
| author_sort | Gabriel Chukwunonso Amaizu |
| collection | DOAJ |
| description | The increasing prevalence of cancer necessitates advanced methodologies for early detection and diagnosis. Early intervention is crucial for improving patient outcomes and reducing the overall burden on healthcare systems. Traditional centralized methods of medical image analysis pose significant risks to patient privacy and data security, as they require the aggregation of sensitive information in a single location. Furthermore, these methods often suffer from limitations related to data diversity and scalability, hindering the development of universally robust diagnostic models. Recent advancements in machine learning, particularly deep learning, have shown promise in enhancing medical image analysis. However, the need to access large and diverse datasets for training these models introduces challenges in maintaining patient confidentiality and adhering to strict data protection regulations. This paper introduces FedViTBloc, a secure and privacy-enhanced framework for medical image analysis utilizing Federated Learning (FL) combined with Vision Transformers (ViT) and blockchain technology. The proposed system ensures patient data privacy and security through fully homomorphic encryption and differential privacy techniques. By employing a decentralized FL approach, multiple medical institutions can collaboratively train a robust deep-learning model without sharing raw data. Blockchain integration further enhances the security and trustworthiness of the FL process by managing client registration and ensuring secure onboarding of participants. Experimental results demonstrate the effectiveness of FedViTBloc in medical image analysis while maintaining stringent privacy standards, achieving 67% accuracy and reducing loss below 2 across 10 clients, ensuring scalability and robustness. |
| format | Article |
| id | doaj-art-3d155eac05ab419dbc9d4ea090e87ea8 |
| institution | Kabale University |
| issn | 2667-2952 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | High-Confidence Computing |
| spelling | doaj-art-3d155eac05ab419dbc9d4ea090e87ea82025-08-20T04:03:21ZengElsevierHigh-Confidence Computing2667-29522025-09-015310030210.1016/j.hcc.2025.100302FedViTBloc: Secure and privacy-enhanced medical image analysis with federated vision transformer and blockchainGabriel Chukwunonso Amaizu0Akshita Maradapu Vera Venkata Sai1Sanjay Bhardwaj2Dong-Seong Kim3Madhuri Siddula4Yingshu Li5Digital Twin Research Group, Department of Computer and Information Sciences, Towson University, Towson 21252, USA; IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, South KoreaDigital Twin Research Group, Department of Computer and Information Sciences, Towson University, Towson 21252, USA; Corresponding author.IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, South KoreaIT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, South KoreaDepartment of Computer Science, North Carolina Agricultural and Technical University, Greensboro 27411, USADepartment of Computer Science, Georgia State University, Atlanta 30303, USAThe increasing prevalence of cancer necessitates advanced methodologies for early detection and diagnosis. Early intervention is crucial for improving patient outcomes and reducing the overall burden on healthcare systems. Traditional centralized methods of medical image analysis pose significant risks to patient privacy and data security, as they require the aggregation of sensitive information in a single location. Furthermore, these methods often suffer from limitations related to data diversity and scalability, hindering the development of universally robust diagnostic models. Recent advancements in machine learning, particularly deep learning, have shown promise in enhancing medical image analysis. However, the need to access large and diverse datasets for training these models introduces challenges in maintaining patient confidentiality and adhering to strict data protection regulations. This paper introduces FedViTBloc, a secure and privacy-enhanced framework for medical image analysis utilizing Federated Learning (FL) combined with Vision Transformers (ViT) and blockchain technology. The proposed system ensures patient data privacy and security through fully homomorphic encryption and differential privacy techniques. By employing a decentralized FL approach, multiple medical institutions can collaboratively train a robust deep-learning model without sharing raw data. Blockchain integration further enhances the security and trustworthiness of the FL process by managing client registration and ensuring secure onboarding of participants. Experimental results demonstrate the effectiveness of FedViTBloc in medical image analysis while maintaining stringent privacy standards, achieving 67% accuracy and reducing loss below 2 across 10 clients, ensuring scalability and robustness.http://www.sciencedirect.com/science/article/pii/S2667295225000066AIBlockchainDecentralizedFederated LearningMedical imagesMachine Learning |
| spellingShingle | Gabriel Chukwunonso Amaizu Akshita Maradapu Vera Venkata Sai Sanjay Bhardwaj Dong-Seong Kim Madhuri Siddula Yingshu Li FedViTBloc: Secure and privacy-enhanced medical image analysis with federated vision transformer and blockchain High-Confidence Computing AI Blockchain Decentralized Federated Learning Medical images Machine Learning |
| title | FedViTBloc: Secure and privacy-enhanced medical image analysis with federated vision transformer and blockchain |
| title_full | FedViTBloc: Secure and privacy-enhanced medical image analysis with federated vision transformer and blockchain |
| title_fullStr | FedViTBloc: Secure and privacy-enhanced medical image analysis with federated vision transformer and blockchain |
| title_full_unstemmed | FedViTBloc: Secure and privacy-enhanced medical image analysis with federated vision transformer and blockchain |
| title_short | FedViTBloc: Secure and privacy-enhanced medical image analysis with federated vision transformer and blockchain |
| title_sort | fedvitbloc secure and privacy enhanced medical image analysis with federated vision transformer and blockchain |
| topic | AI Blockchain Decentralized Federated Learning Medical images Machine Learning |
| url | http://www.sciencedirect.com/science/article/pii/S2667295225000066 |
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