השוואת שיטות

	למידה מאוחדת מבוססת אנסמבל	ערימה
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2017–2019	1992
הוגה השיטה	McMahan et al. (FedAvg) extended by subsequent ensemble work	Wolpert, D.H.
סוג	Ensemble meta-strategy over federated clients	Ensemble (heterogeneous meta-learning)
מקור מכונן	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.
כינויים	federated ensemble learning, EFL, federated model ensembling, federated multi-model aggregation	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
קשורות	6	5
תקציר	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.