Сравнение методов

	Байесовское федеративное обучение	Байесовский перенос знаний
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2019	2006–2010
Автор метода	Yurochkin, M. et al.; McMahan, H. B. et al. (foundational federated learning)	Raina, R.; Ng, A. Y.; Koller, D. (and subsequent community)
Тип	Probabilistic federated ensemble	Probabilistic transfer / domain adaptation framework
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.	Raina, R., Ng, A. Y., & Koller, D. (2006). Constructing informative priors using transfer learning. In Proceedings of the 23rd International Conference on Machine Learning (ICML), pp. 713–720. ACM.
Другие названия	BFL, probabilistic federated learning, Bayesian nonparametric federated learning, federated Bayesian inference	BTL, Bayesian domain adaptation, probabilistic transfer learning, Bayesian knowledge transfer
Связанные	5	4
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.	Bayesian Transfer Learning is a probabilistic framework that uses knowledge from a data-rich source domain to construct informative priors for a model trained on a data-scarce target domain. By encoding source-domain knowledge as prior distributions over parameters, the framework lets the model generalize well on the target task even with very limited labeled examples.