方法对比
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| 半监督联邦学习× | 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≠ | 2020 | 2010 (formalized); 1990s (early roots) |
| 提出者≠ | Jeong, W. et al. / multiple independent groups | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| 类型≠ | Distributed semi-supervised learning framework | Learning paradigm |
| 开创性文献≠ | 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 ↗ | 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 | SSL-FL, federated semi-supervised learning, FSSL, semi-supervised distributed learning | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| 相关≠ | 6 | 3 |
| 摘要≠ | 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. | 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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