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| Федеративно активно обучение× | Трансферно обучение× | |
|---|---|---|
| Област | Машинно обучение | Машинно обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2020s | 2010 (formalized); 1990s (early roots) |
| Създател≠ | Multiple authors (federated active learning emerged ~2020) | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Тип≠ | Hybrid paradigm (active querying within distributed training) | Learning paradigm |
| Основополагащ източник≠ | 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 ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| Други названия | Federated Active Learning, FAL, Active Federated Learning, distributed active learning | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Свързани≠ | 6 | 3 |
| Резюме≠ | 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. | Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond. |
| ScholarGateНабор от данни ↗ |
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