Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Активное обучение с федеративным обучением× | Обучение с частичной разметкой× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2020s | 1970s–2006 (formalized) |
| Автор метода≠ | Multiple authors (federated active learning emerged ~2020) | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Тип≠ | Hybrid paradigm (active querying within distributed training) | Learning paradigm |
| Основополагающий источник≠ | 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 ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Другие названия | Federated Active Learning, FAL, Active Federated Learning, distributed active learning | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Связанные≠ | 6 | 5 |
| Сводка≠ | 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. | Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained. |
| ScholarGateНабор данных ↗ |
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