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...

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Detalles Bibliográficos
Autores principales: Qi, Tao, Wu, Fangzhao, Wu, Chuhan, He, Liang, Huang, Yongfeng, Xie, Xing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
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

Internet

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

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