方法对比
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| 贝叶斯联邦学习× | 高斯过程× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2019 | 2006 (book); roots in Kriging, 1951) |
| 提出者≠ | Yurochkin, M. et al.; McMahan, H. B. et al. (foundational federated learning) | Rasmussen, C. E. & Williams, C. K. I. |
| 类型≠ | Probabilistic federated ensemble | Probabilistic non-parametric model |
| 开创性文献≠ | 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 ↗ | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 |
| 别名 | BFL, probabilistic federated learning, Bayesian nonparametric federated learning, federated Bayesian inference | GP, Gaussian Process Regression, GPR, Kriging |
| 相关≠ | 5 | 3 |
| 摘要≠ | 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. |
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