Methoden vergleichen

	Ensemble Federated Learning	Stacking
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2017–2019	1992
Urheber	McMahan et al. (FedAvg) extended by subsequent ensemble work	Wolpert, D.H.
Typ	Ensemble meta-strategy over federated clients	Ensemble (heterogeneous meta-learning)
Wegweisende Quelle	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.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
Aliasnamen	federated ensemble learning, EFL, federated model ensembling, federated multi-model aggregation	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
Verwandt	6	5
Zusammenfassung	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.	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.