Vertaile menetelmiä

	Aktiivinen oppiminen ja hajautettu oppiminen	Hajautettu oppiminen
Tieteenala	Koneoppiminen	Yksityisyydensuoja
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2020s	2017
Kehittäjä	Multiple authors (federated active learning emerged ~2020)	McMahan et al.
Tyyppi	Hybrid paradigm (active querying within distributed training)	Distributed privacy-preserving machine learning
Alkuperäislähde	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).	McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics, 1273–1282.
Rinnakkaisnimet	Federated Active Learning, FAL, Active Federated Learning, distributed active learning	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
Liittyvät	6	3
Tiivistelmä	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.	Federated Learning is a distributed machine learning paradigm introduced by McMahan et al. in 2017 in which a global model is trained collaboratively across multiple decentralized clients — such as mobile devices or hospital systems — without ever transferring raw data to a central server. Each participant computes model updates locally using its private data; only those updates, not the underlying data, are communicated and aggregated by the server to improve the shared model.