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| 베이지안 연합 학습× | 연합 학습× | |
|---|---|---|
| 분야≠ | 머신러닝 | 프라이버시 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2019 | 2017 |
| 창시자≠ | Yurochkin, M. et al.; McMahan, H. B. et al. (foundational federated learning) | McMahan et al. |
| 유형≠ | Probabilistic federated ensemble | Distributed privacy-preserving machine learning |
| 원전≠ | 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 ↗ | McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics, 1273–1282. link ↗ |
| 별칭 | BFL, probabilistic federated learning, Bayesian nonparametric federated learning, federated Bayesian inference | Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme |
| 관련≠ | 5 | 3 |
| 요약≠ | 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. | Federated Learning is a distributed machine learning paradigm introduced by McMahan et al. in 2017 in which a global model is trained collaboratively across multiple decentralized clients — such as mobile devices or hospital systems — without ever transferring raw data to a central server. Each participant computes model updates locally using its private data; only those updates, not the underlying data, are communicated and aggregated by the server to improve the shared model. |
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