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联邦主动学习

联邦主动学习结合了主动学习的标注效率和联邦学习的隐私保护去中心化。一个共享的全局模型在分布式客户端上进行训练，每个客户端独立地对其未标记的本地数据进行排序，并仅请求最具信息量示例的标签，同时在设备上保留原始数据。

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Method map

The neighbourhood of related methods — select a node to explore.

联邦主动学习
主动学习联邦学习在线学习自监督学习半监督学习迁移学习

来源

  1. 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 ↗
  2. Federated learning. Wikipedia. link ↗

如何引用本页

ScholarGate. (2026, June 3). Federated Active Learning (Active Learning within Federated Learning). ScholarGate. https://scholargate.app/zh/machine-learning/active-learning-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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ScholarGate — Active Learning Federated Learning (Federated Active Learning (Active Learning within Federated Learning)). 于 2026-06-15 检索自 https://scholargate.app/zh/machine-learning/active-learning-federated-learning · 数据集： https://doi.org/10.5281/zenodo.20539026