方法对比
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| 集成联邦学习× | 迁移学习× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2017–2019 | 2010 (formalized); 1990s (early roots) |
| 提出者≠ | McMahan et al. (FedAvg) extended by subsequent ensemble work | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| 类型≠ | Ensemble meta-strategy over federated clients | Learning paradigm |
| 开创性文献≠ | McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 54, 1273–1282. link ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| 别名 | federated ensemble learning, EFL, federated model ensembling, federated multi-model aggregation | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| 相关≠ | 6 | 3 |
| 摘要≠ | Ensemble Federated Learning combines the privacy-preserving distribution of federated learning with ensemble aggregation: each participating client trains its own local model on private data, and the server aggregates predictions — or model parameters — from all clients using ensemble strategies such as voting, averaging, or stacking, instead of simple parameter averaging alone. | Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond. |
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