手法を比較

	アンサンブル連合学習	ブースティング
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	2017–2019	1990–1997
提唱者	McMahan et al. (FedAvg) extended by subsequent ensemble work	Schapire, R. E.; Freund, Y.
種類	Ensemble meta-strategy over federated clients	Sequential ensemble (iterative reweighting)
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.	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.
別名	federated ensemble learning, EFL, federated model ensembling, federated multi-model aggregation	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
関連	6	6
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.	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.