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| Semi-überwachtes Föderiertes Lernen× | Semi-Supervised Learning× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2020 | 1970s–2006 (formalized) |
| Urheber≠ | Jeong, W. et al. / multiple independent groups | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Typ≠ | Distributed semi-supervised learning framework | Learning paradigm |
| Wegweisende Quelle≠ | Jeong, W., Yoon, J., Yang, E., & Hwang, S. J. (2020). Federated Semi-Supervised Learning with Inter-Client Consistency. International Conference on Learning Representations (ICLR 2021). link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Aliasnamen | SSL-FL, federated semi-supervised learning, FSSL, semi-supervised distributed learning | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | Semi-supervised federated learning (SSFL) trains a shared model across many decentralized clients — each holding private data — when only a subset of clients or a subset of local samples carry labels. It combines the privacy-preserving coordination of federated learning with the label-efficiency of semi-supervised techniques such as pseudo-labeling and consistency regularization, enabling strong model quality without centralizing sensitive data. | Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained. |
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