Comparer des méthodes

	Apprentissage fédéré semi-supervisé	Apprentissage à peu d'exemples
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2020	2011–2017
Auteur d'origine	Jeong, W. et al. / multiple independent groups	Lake, B. M.; Vinyals, O.; Finn, C. et al.
Type	Distributed semi-supervised learning framework	Meta-learning / low-data learning paradigm
Source fondatrice	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).	Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., & Kavukcuoglu, K. (2016). Matching Networks for One Shot Learning. Advances in Neural Information Processing Systems (NeurIPS), 29.
Alias	SSL-FL, federated semi-supervised learning, FSSL, semi-supervised distributed learning	FSL, low-shot learning, k-shot learning, meta-learning for few examples
Apparentées	6	4
Résumé	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.	Few-shot learning is a machine learning paradigm that trains models to recognize new classes or solve new tasks from only a handful of labeled examples — typically one to five — by leveraging prior knowledge acquired from a large, related training distribution. It is especially relevant in domains where labeling is expensive, scarce, or structurally limited.