方法对比
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| 鲁棒联邦学习× | 半监督联邦学习× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2017 | 2020 |
| 提出者≠ | Blanchard, P.; El Mhamdi, E. M.; Guerraoui, R. | Jeong, W. et al. / multiple independent groups |
| 类型≠ | Distributed learning with Byzantine-tolerant aggregation | Distributed semi-supervised learning framework |
| 开创性文献≠ | Blanchard, P., El Mhamdi, E. M., Guerraoui, R., & Stainer, J. (2017). Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent. Advances in Neural Information Processing Systems, 30. link ↗ | 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 ↗ |
| 别名 | Byzantine-robust federated learning, fault-tolerant federated learning, robust FL, Byzantine-tolerant distributed learning | SSL-FL, federated semi-supervised learning, FSSL, semi-supervised distributed learning |
| 相关 | 6 | 6 |
| 摘要≠ | Robust Federated Learning extends standard federated learning with Byzantine-tolerant aggregation rules that protect the global model against malicious, corrupted, or unreliable clients. Instead of naively averaging client gradients, robust aggregation methods such as coordinate-wise median or Krum filter out harmful updates so that a minority of adversarial participants cannot derail training. | 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. |
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