Trustworthiness Assurance for Model Updates in Federated Learning
<p>Training accurate Machine Learning (ML) models requires access to large volumes of data, often personal or confidential, raising significant privacy concerns under certain rules and regulations. Traditional protection methods, such as data encryption, are effective in protecting data content but...
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| Autores principales: | , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2026
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/1235771 |
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