Comparer des méthodes

	Apprentissage Fédéré en Ensemble	Bagging (Bootstrap Aggregating)
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2017–2019	1996
Auteur d'origine	McMahan et al. (FedAvg) extended by subsequent ensemble work	Breiman, L.
Type	Ensemble meta-strategy over federated clients	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
Source fondatrice	McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 54, 1273–1282.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.
Alias	federated ensemble learning, EFL, federated model ensembling, federated multi-model aggregation	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
Apparentées	6	5
Résumé	Ensemble Federated Learning combines the privacy-preserving distribution of federated learning with ensemble aggregation: each participating client trains its own local model on private data, and the server aggregates predictions — or model parameters — from all clients using ensemble strategies such as voting, averaging, or stacking, instead of simple parameter averaging alone.	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.