방법 비교

	앙상블 연합 학습	부스팅	연합 학습
분야	머신러닝	머신러닝	프라이버시
계열	Machine learning	Machine learning	Machine learning
기원 연도	2017–2019	1990–1997	2017
창시자	McMahan et al. (FedAvg) extended by subsequent ensemble work	Schapire, R. E.; Freund, Y.	McMahan et al.
유형	Ensemble meta-strategy over federated clients	Sequential ensemble (iterative reweighting)	Distributed privacy-preserving machine learning
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.	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.
별칭	federated ensemble learning, EFL, federated model ensembling, federated multi-model aggregation	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
관련	6	6	3
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.	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.