So sánh phương pháp

	Học Liên kết Bayes (Bayesian Federated Learning) kết hợp học liên kết	Quá trình Gauss
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2019	2006 (book); roots in Kriging, 1951)
Người khởi xướng	Yurochkin, M. et al.; McMahan, H. B. et al. (foundational federated learning)	Rasmussen, C. E. & Williams, C. K. I.
Loại	Probabilistic federated ensemble	Probabilistic non-parametric model
Công trình gốc	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.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Tên gọi khác	BFL, probabilistic federated learning, Bayesian nonparametric federated learning, federated Bayesian inference	GP, Gaussian Process Regression, GPR, Kriging
Liên quan	5	3
Tóm tắt	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.	A Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single value, it returns a predictive mean and a calibrated uncertainty estimate at every test point, making it especially valuable for regression on small to medium datasets and for Bayesian optimization tasks.