手法を比較

	アンサンブル連合学習	Federated Learning（連合学習）
分野	機械学習	プライバシー
系統	Machine learning	Machine learning
提唱年	2017–2019	2017
提唱者	McMahan et al. (FedAvg) extended by subsequent ensemble work	McMahan et al.
種類	Ensemble meta-strategy over federated clients	Distributed privacy-preserving machine learning
原典	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.
別名	federated ensemble learning, EFL, federated model ensembling, federated multi-model aggregation	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
関連	6	3
概要	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.