Equitable Federated Learning with NCA
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918072396283904 |
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| author | Lemke, Nick Konstantin, Mirko Krumb, Henry John Kalkhof, John Stieber, Jonathan Mukhopadhyay, Anirban |
| author_facet | Lemke, Nick Konstantin, Mirko Krumb, Henry John Kalkhof, John Stieber, Jonathan Mukhopadhyay, Anirban |
| contents | Federated Learning (FL) is enabling collaborative model training across institutions without sharing sensitive patient data. This approach is particularly valuable in low- and middle-income countries (LMICs), where access to trained medical professionals is limited. However, FL adoption in LMICs faces significant barriers, including limited high-performance computing resources and unreliable internet connectivity. To address these challenges, we introduce FedNCA, a novel FL system tailored for medical image segmentation tasks. FedNCA leverages the lightweight Med-NCA architecture, enabling training on low-cost edge devices, such as widely available smartphones, while minimizing communication costs. Additionally, our encryption-ready FedNCA proves to be suitable for compromised network communication. By overcoming infrastructural and security challenges, FedNCA paves the way for inclusive, efficient, lightweight, and encryption-ready medical imaging solutions, fostering equitable healthcare advancements in resource-constrained regions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21735 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Equitable Federated Learning with NCA Lemke, Nick Konstantin, Mirko Krumb, Henry John Kalkhof, John Stieber, Jonathan Mukhopadhyay, Anirban Computer Vision and Pattern Recognition Federated Learning (FL) is enabling collaborative model training across institutions without sharing sensitive patient data. This approach is particularly valuable in low- and middle-income countries (LMICs), where access to trained medical professionals is limited. However, FL adoption in LMICs faces significant barriers, including limited high-performance computing resources and unreliable internet connectivity. To address these challenges, we introduce FedNCA, a novel FL system tailored for medical image segmentation tasks. FedNCA leverages the lightweight Med-NCA architecture, enabling training on low-cost edge devices, such as widely available smartphones, while minimizing communication costs. Additionally, our encryption-ready FedNCA proves to be suitable for compromised network communication. By overcoming infrastructural and security challenges, FedNCA paves the way for inclusive, efficient, lightweight, and encryption-ready medical imaging solutions, fostering equitable healthcare advancements in resource-constrained regions. |
| title | Equitable Federated Learning with NCA |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.21735 |