Machine learningMachine learning
半监督联邦学习
半监督联邦学习（SSFL）在只有部分客户端或部分本地样本带有标签的情况下，跨多个拥有私有数据的去中心化客户端训练一个共享模型。它将联邦学习的隐私保护协调能力与伪标签和一致性正则化等半监督技术的标签效率相结合，能够在不集中敏感数据的情况下实现强大的模型质量。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- 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 ↗
- Zhang, Z., Chen, Y., Yu, H., & Lu, J. (2021). SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling. arXiv preprint arXiv:2108.09412. link ↗
如何引用本页
ScholarGate. (2026, June 3). Semi-supervised Federated Learning. ScholarGate. https://scholargate.app/zh/machine-learning/semi-supervised-federated-learning
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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