方法对比

	Bagging（Bootstrap Aggregating）	联邦学习
领域	机器学习	隐私
方法族	Machine learning	Machine learning
起源年份	1996	2017
提出者	Breiman, L.	McMahan et al.
类型	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Distributed privacy-preserving machine learning
开创性文献	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	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
相关	5	3
摘要	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.