পদ্ধতির তুলনা করুন

	Bagging (Bootstrap Aggregating)	ফেডারেটেড লার্নিং	স্ট্যাকিং
ক্ষেত্র	যন্ত্র শিখন	গোপনীয়তা	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning	Machine learning
উদ্ভবের বছর	1996	2017	1992
প্রবর্তক	Breiman, L.	McMahan et al.	Wolpert, D.H.
ধরন	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Distributed privacy-preserving machine learning	Ensemble (heterogeneous meta-learning)
মৌলিক উৎস	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.
অপর নাম	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
সম্পর্কিত	5	3	5
সারসংক্ষেপ	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.