مقایسهٔ روش‌ها

	یادگیری فدرال	چیدمان
حوزه	حریم خصوصی	یادگیری ماشین
خانواده	Machine learning	Machine learning
سال پیدایش	2017	1992
پدیدآور	McMahan et al.	Wolpert, D.H.
نوع	Distributed privacy-preserving machine learning	Ensemble (heterogeneous meta-learning)
منبع بنیادین	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	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
مرتبط	3	5
خلاصه	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.