Сравнение методов

	Бустинг	Федеративное обучение
Область	Машинное обучение	Конфиденциальность
Семейство	Machine learning	Machine learning
Год появления	1990–1997	2017
Автор метода	Schapire, R. E.; Freund, Y.	McMahan et al.
Тип	Sequential ensemble (iterative reweighting)	Distributed privacy-preserving machine learning
Основополагающий источник	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	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.
Другие названия	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
Связанные	6	3
Сводка	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	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.