Salīdzināt metodes

	Federated apvienojums (Ensemble Federated Learning)	Federatīvā apmācība	Stacking
Nozare	Mašīnmācīšanās	Privātums	Mašīnmācīšanās
Saime	Machine learning	Machine learning	Machine learning
Izcelsmes gads	2017–2019	2017	1992
Autors	McMahan et al. (FedAvg) extended by subsequent ensemble work	McMahan et al.	Wolpert, D.H.
Tips	Ensemble meta-strategy over federated clients	Distributed privacy-preserving machine learning	Ensemble (heterogeneous meta-learning)
Pirmavots	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.	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.
Citi nosaukumi	federated ensemble learning, EFL, federated model ensembling, federated multi-model aggregation	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
Saistītās	6	3	5
Kopsavilkums	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.	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.