مقایسهٔ روش‌ها

	بگینگ (تجمیع بوت‌استرپ)	یادگیری فدرال	چیدمان
حوزه	یادگیری ماشین	حریم خصوصی	یادگیری ماشین
خانواده	Machine learning	Machine learning	Machine learning
سال پیدایش	1996	2017	1992
پدیدآور	Breiman, L.	McMahan et al.	Wolpert, D.H.
نوع	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Distributed privacy-preserving machine learning	Ensemble (heterogeneous meta-learning)
منبع بنیادین	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.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
نام‌های دیگر	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
مرتبط	5	3	5
خلاصه	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.	Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions.