方法对比
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| 半监督联邦学习× | 在线联邦学习× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2020 | 2019–2021 |
| 提出者≠ | Jeong, W. et al. / multiple independent groups | McMahan, B. et al. (FL foundation); extended to online setting by multiple researchers c. 2019–2021 |
| 类型≠ | Distributed semi-supervised learning framework | Distributed sequential learning |
| 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 ↗ | Damaskinos, G., Guerraoui, R., Kermarrec, A.-M., Guirguis, A., Riviere, M., & Tempo, R. (2020). FLEET: Flexible and Efficient Federated Learning for Edge AI. Proceedings of Machine Learning and Systems (MLSys). link ↗ |
| 别名 | SSL-FL, federated semi-supervised learning, FSSL, semi-supervised distributed learning | OFL, federated online learning, streaming federated learning, real-time federated learning |
| 相关≠ | 6 | 5 |
| 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. | Online Federated Learning (OFL) combines the privacy-preserving, decentralised structure of federated learning with the sequential, sample-by-sample update regime of online learning. Clients — such as mobile devices or edge sensors — receive a global model, update it on newly arriving local data without sharing raw observations, and contribute compressed updates to a central server that aggregates them in near-real-time. |
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