Differentially private knowledge transfer for federated learning
Abstract 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...
| Auteurs principaux: | , , , , , |
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| Format: | Article |
| Langue: | English |
| Publié: |
Nature Portfolio
2023-06-01
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| Collection: | Nature Communications |
| Accès en ligne: | https://doi.org/10.1038/s41467-023-38794-x |