方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 联邦主动学习× | 主动学习× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2020s | 2009 |
| 提出者≠ | Multiple authors (federated active learning emerged ~2020) | Burr Settles |
| 类型≠ | Hybrid paradigm (active querying within distributed training) | Interactive supervised learning framework |
| 开创性文献≠ | Ro, J. Y., Ali, A., Lin, Z., & Suresh, A. T. (2021). Scaling Federated Learning for Fine-tuning of Large Language Models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP). link ↗ | Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648. link ↗ |
| 别名 | Federated Active Learning, FAL, Active Federated Learning, distributed active learning | Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme |
| 相关≠ | 6 | 2 |
| 摘要≠ | Federated Active Learning combines the annotation-efficiency of active learning with the privacy-preserving decentralization of federated learning. A shared global model is trained across distributed clients, each of which independently ranks its unlabeled local data and requests labels only for the most informative examples, keeping raw data on-device throughout. | Active learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informative unlabeled examples. Formalized by Burr Settles in his seminal 2009 literature survey, active learning addresses the practical bottleneck of annotation cost by achieving high model accuracy with far fewer labeled examples than passive supervised learning requires. |
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