方法对比
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| 贝叶斯联邦学习× | 半监督联邦学习× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2019 | 2020 |
| 提出者≠ | Yurochkin, M. et al.; McMahan, H. B. et al. (foundational federated learning) | Jeong, W. et al. / multiple independent groups |
| 类型≠ | Probabilistic federated ensemble | Distributed semi-supervised learning framework |
| 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 ↗ | Jeong, W., Yoon, J., Yang, E., & Hwang, S. J. (2020). Federated Semi-Supervised Learning with Inter-Client Consistency. International Conference on Learning Representations (ICLR 2021). link ↗ |
| 别名 | BFL, probabilistic federated learning, Bayesian nonparametric federated learning, federated Bayesian inference | SSL-FL, federated semi-supervised learning, FSSL, semi-supervised distributed learning |
| 相关≠ | 5 | 6 |
| 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. | Semi-supervised federated learning (SSFL) trains a shared model across many decentralized clients — each holding private data — when only a subset of clients or a subset of local samples carry labels. It combines the privacy-preserving coordination of federated learning with the label-efficiency of semi-supervised techniques such as pseudo-labeling and consistency regularization, enabling strong model quality without centralizing sensitive data. |
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