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| Bayesian Federated Learning× | Bayesovská logistická regresia× | |
|---|---|---|
| Odbor≠ | Strojové učenie | Bayesovské metódy |
| Rodina≠ | Machine learning | Bayesian methods |
| Rok vzniku≠ | 2019 | 2008 |
| Tvorca≠ | Yurochkin, M. et al.; McMahan, H. B. et al. (foundational federated learning) | Gelman, Jakulin, Pittau & Su (weakly-informative prior framework, 2008) |
| Typ≠ | Probabilistic federated ensemble | Bayesian classification model |
| Pôvodný zdroj≠ | 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. link ↗ | 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. DOI ↗ |
| Ďalšie názvy≠ | BFL, probabilistic federated learning, Bayesian nonparametric federated learning, federated Bayesian inference | bayesian binary logistic regression, bayesian classification model, Bayesian Lojistik Regresyon |
| Príbuzné≠ | 5 | 3 |
| Zhrnutie≠ | 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. |
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