Comparer des méthodes

	Apprentissage Actif Fédéré	Apprentissage Fédéré
Domaine	Apprentissage automatique	Protection de la vie privée
Famille	Machine learning	Machine learning
Année d'origine	2020s	2017
Auteur d'origine	Multiple authors (federated active learning emerged ~2020)	McMahan et al.
Type	Hybrid paradigm (active querying within distributed training)	Distributed privacy-preserving machine learning
Source fondatrice	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.
Alias	Federated Active Learning, FAL, Active Federated Learning, distributed active learning	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
Apparentées	6	3
Résumé	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.