方法对比

	集成联邦学习	Bagging（Bootstrap Aggregating）	联邦学习
领域	机器学习	机器学习	隐私
方法族	Machine learning	Machine learning	Machine learning
起源年份	2017–2019	1996	2017
提出者	McMahan et al. (FedAvg) extended by subsequent ensemble work	Breiman, L.	McMahan et al.
类型	Ensemble meta-strategy over federated clients	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Distributed privacy-preserving machine learning
开创性文献	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.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	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.
别名	federated ensemble learning, EFL, federated model ensembling, federated multi-model aggregation	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
相关	6	5	3
摘要	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.	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	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.