Comparer des méthodes

	Boosting	Apprentissage Fédéré
Domaine	Apprentissage automatique	Protection de la vie privée
Famille	Machine learning	Machine learning
Année d'origine	1990–1997	2017
Auteur d'origine	Schapire, R. E.; Freund, Y.	McMahan et al.
Type	Sequential ensemble (iterative reweighting)	Distributed privacy-preserving machine learning
Source fondatrice	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics, 1273–1282.
Alias	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
Apparentées	6	3
Résumé	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	Federated Learning is a distributed machine learning paradigm introduced by McMahan et al. in 2017 in which a global model is trained collaboratively across multiple decentralized clients — such as mobile devices or hospital systems — without ever transferring raw data to a central server. Each participant computes model updates locally using its private data; only those updates, not the underlying data, are communicated and aggregated by the server to improve the shared model.