Cargando…
Differentially private knowledge transfer for federated learning
Extracting useful knowledge from big data is important for machine learning. When data is privacy-sensitive and cannot be directly collected, federated learning is a promising option that extracts knowledge from decentralized data by learning and exchanging model parameters, rather than raw data. Ho...
| Autores principales: | , , , , , |
|---|---|
| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2023
|
| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10290720/ https://www.ncbi.nlm.nih.gov/pubmed/37355643 http://dx.doi.org/10.1038/s41467-023-38794-x |