Machine learningMachine learning
贝叶斯联邦学习
贝叶斯联邦学习将联邦学习——模型训练分布在多个客户端上，而不共享原始数据——与贝叶斯推断相结合，因此每个客户端维护模型参数的后验分布，而不是单一的点估计。这产生了原则性的不确定性量化以及在异构、隐私保护的数据孤岛上更鲁棒的模型聚合。
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来源
- 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 ↗
- Corinzia, L., & Buhmann, J. M. (2019). Variational Federated Multi-Task Learning. arXiv preprint arXiv:1906.06268. link ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Federated Learning (Probabilistic Federated Model Aggregation). ScholarGate. https://scholargate.app/zh/machine-learning/bayesian-federated-learning
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