Towards privacy-preserving Alzheimer’s disease classification: Federated learning on T1-weighted magnetic resonance imaging data
Objective This study aims to address the challenge of privacy-preserving Alzheimer’s disease classification using federated learning across various data distributions, focusing on real-world applicability. The goal is to improve the efficiency of classification by minimizing communication rounds bet...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2024-11-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076241295577 |
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