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Federated Learning Techniques for Secure and Accurate Diabetes
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| dc.contributor.author | Azeri, Nabila | |
| dc.contributor.author | Ouided, Hioual1; Benmerzoug, Djamel2; Hioual, Ouassila | |
| dc.date.accessioned | 2025-03-17T09:24:44Z | |
| dc.date.available | 2025-03-17T09:24:44Z | |
| dc.date.issued | 25/10/2024 | |
| dc.identifier.uri | http://depot.umc.edu.dz/handle/123456789/14521 | |
| dc.description.abstract | Diabetes is a growing global health concern, with a significant rise in prevalence over the past few decades. Traditional machine learning approaches for diabetes prediction often involve centralizing sensitive patient data, which poses significant privacy and security risks | fr_FR |
| dc.publisher | Université Frères Mentouri - Constantine 1 | |
| dc.title | Federated Learning Techniques for Secure and Accurate Diabetes | fr_FR |
