Comparer des méthodes

	Apprentissage Fédéré en Ensemble	Ensemble par vote
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2017–2019	1990s–2004
Auteur d'origine	McMahan et al. (FedAvg) extended by subsequent ensemble work	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Type	Ensemble meta-strategy over federated clients	Ensemble (combination of multiple classifiers by vote)
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.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Alias	federated ensemble learning, EFL, federated model ensembling, federated multi-model aggregation	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
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.	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.