পদ্ধতির তুলনা করুন

	Bayesian Federated Learning	বেইসিয়ান লজিস্টিক রিগ্রেশন
ক্ষেত্র	যন্ত্র শিখন	বেইসীয়
পরিবার	Machine learning	Bayesian methods
উদ্ভবের বছর	2019	2008
প্রবর্তক	Yurochkin, M. et al.; McMahan, H. B. et al. (foundational federated learning)	Gelman, Jakulin, Pittau & Su (weakly-informative prior framework, 2008)
ধরন	Probabilistic federated ensemble	Bayesian classification model
মৌলিক উৎস	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.	Gelman, A., Jakulin, A., Pittau, M. G. & Su, Y.-S. (2008). A Weakly Informative Default Prior Distribution for Logistic and Other Regression Models. Annals of Applied Statistics, 2(4), 1360–1383.
অপর নাম	BFL, probabilistic federated learning, Bayesian nonparametric federated learning, federated Bayesian inference	bayesian binary logistic regression, bayesian classification model, Bayesian Lojistik Regresyon
সম্পর্কিত	5	3
সারসংক্ষেপ	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 logistic regression is a classification model that applies Bayesian inference to a logistic (sigmoid) likelihood for binary or multinomial outcomes. Developed within the weakly-informative prior framework formalised by Gelman, Jakulin, Pittau and Su (2008), it places a prior distribution over the coefficients and combines that prior with the data likelihood to yield a full posterior distribution for each parameter — delivering calibrated class probabilities and honest uncertainty even in small samples, rare-event settings, or cases of complete separation where frequentist maximum likelihood estimation collapses.