השוואת שיטות

	למידה מאוחדת בייסיאנית	למידת העברה בייסיאנית
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2019	2006–2010
הוגה השיטה	Yurochkin, M. et al.; McMahan, H. B. et al. (foundational federated learning)	Raina, R.; Ng, A. Y.; Koller, D. (and subsequent community)
סוג	Probabilistic federated ensemble	Probabilistic transfer / domain adaptation framework
מקור מכונן	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.
כינויים	BFL, probabilistic federated learning, Bayesian nonparametric federated learning, federated Bayesian inference	BTL, Bayesian domain adaptation, probabilistic transfer learning, Bayesian knowledge transfer
קשורות	5	4
תקציר	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.