Porovnat metody

	Zesilování	Federované učení	Stacking
Obor	Strojové učení	Soukromí	Strojové učení
Rodina	Machine learning	Machine learning	Machine learning
Rok vzniku	1990–1997	2017	1992
Tvůrce	Schapire, R. E.; Freund, Y.	McMahan et al.	Wolpert, D.H.
Typ	Sequential ensemble (iterative reweighting)	Distributed privacy-preserving machine learning	Ensemble (heterogeneous meta-learning)
Původní zdroj	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.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
Další názvy	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
Příbuzné	6	3	5
Shrnutí	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.	Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions.