المستودع الرقمي في جامعة الإخوة منتوري قسنطينة 1
Federated Learning Techniques for Secure and Accurate Diabetes
JavaScript is disabled for your browser. Some features of this site may not work without it.
| dc.contributor.author | Azeri, Nabila | |
| dc.date.accessioned | 2025-03-17T09:24:44Z | |
| dc.date.available | 2025-03-17T09:24:44Z | |
| dc.date.issued | 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.title | Federated Learning Techniques for Secure and Accurate Diabetes | fr_FR |
