방법 비교

	앙상블 연합 학습	Voting Ensemble
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2017–2019	1990s–2004
창시자	McMahan et al. (FedAvg) extended by subsequent ensemble work	Lam & Suen; Kuncheva, L. I. (systematic treatment)
유형	Ensemble meta-strategy over federated clients	Ensemble (combination of multiple classifiers by vote)
원전	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.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
별칭	federated ensemble learning, EFL, federated model ensembling, federated multi-model aggregation	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
관련	6	5
요약	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.	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.