Comparer des méthodes

	Apprentissage Fédéré Bayésien	Processus Gaussien
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2019	2006 (book); roots in Kriging, 1951)
Auteur d'origine	Yurochkin, M. et al.; McMahan, H. B. et al. (foundational federated learning)	Rasmussen, C. E. & Williams, C. K. I.
Type	Probabilistic federated ensemble	Probabilistic non-parametric model
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.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Alias	BFL, probabilistic federated learning, Bayesian nonparametric federated learning, federated Bayesian inference	GP, Gaussian Process Regression, GPR, Kriging
Apparentées	5	3
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.	A Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single value, it returns a predictive mean and a calibrated uncertainty estimate at every test point, making it especially valuable for regression on small to medium datasets and for Bayesian optimization tasks.