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| Federated Active Learning (Federated Active Learning)× | Tiešsaistes apguve× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2020s | 1958–2000s |
| Autors≠ | Multiple authors (federated active learning emerged ~2020) | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) |
| Tips≠ | Hybrid paradigm (active querying within distributed training) | Learning paradigm (sequential model update) |
| Pirmavots≠ | 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 ↗ | Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ |
| Citi nosaukumi | Federated Active Learning, FAL, Active Federated Learning, distributed active learning | incremental learning, sequential learning, streaming learning, online machine learning |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | 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. | Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight. |
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