Comparer des méthodes

	Apprentissage Fédéré Bayésien	Apprentissage fédéré semi-supervisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2019	2020
Auteur d'origine	Yurochkin, M. et al.; McMahan, H. B. et al. (foundational federated learning)	Jeong, W. et al. / multiple independent groups
Type	Probabilistic federated ensemble	Distributed semi-supervised learning framework
Source fondatrice	Yurochkin, M., Agarwal, M., Ghosh, S., Greenewald, K., Hoang, N., & Khazaeni, Y. (2019). Bayesian Nonparametric Federated Learning of Neural Networks. Proceedings of the 36th International Conference on Machine Learning (ICML 2019), PMLR 97, 7101–7110.	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).
Alias	BFL, probabilistic federated learning, Bayesian nonparametric federated learning, federated Bayesian inference	SSL-FL, federated semi-supervised learning, FSSL, semi-supervised distributed learning
Apparentées	5	6
Résumé	Bayesian Federated Learning combines federated learning — where model training is distributed across multiple clients without sharing raw data — with Bayesian inference, so that each client maintains a posterior distribution over model parameters rather than a single point estimate. This yields principled uncertainty quantification and more robust model aggregation across heterogeneous, privacy-preserving data silos.	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.