Сравнение методов

	Байесовское федеративное обучение	Федеративное обучение
Область	Машинное обучение	Конфиденциальность
Семейство	Machine learning	Machine learning
Год появления	2019	2017
Автор метода	Yurochkin, M. et al.; McMahan, H. B. et al. (foundational federated learning)	McMahan et al.
Тип	Probabilistic federated ensemble	Distributed privacy-preserving machine learning
Основополагающий источник	Yurochkin, M., Agarwal, M., Ghosh, S., Greenewald, K., Hoang, N., & Khazaeni, Y. (2019). Bayesian Nonparametric Federated Learning of Neural Networks. Proceedings of the 36th International Conference on Machine Learning (ICML 2019), PMLR 97, 7101–7110.	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	BFL, probabilistic federated learning, Bayesian nonparametric federated learning, federated Bayesian inference	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
Связанные	5	3
Сводка	Bayesian Federated Learning combines federated learning — where model training is distributed across multiple clients without sharing raw data — with Bayesian inference, so that each client maintains a posterior distribution over model parameters rather than a single point estimate. This yields principled uncertainty quantification and more robust model aggregation across heterogeneous, privacy-preserving data silos.	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.