Comparer des méthodes

	Apprentissage Fédéré en Ligne	Apprentissage Fédéré
Domaine	Apprentissage automatique	Protection de la vie privée
Famille	Machine learning	Machine learning
Année d'origine	2019–2021	2017
Auteur d'origine	McMahan, B. et al. (FL foundation); extended to online setting by multiple researchers c. 2019–2021	McMahan et al.
Type	Distributed sequential learning	Distributed privacy-preserving machine learning
Source fondatrice	Damaskinos, G., Guerraoui, R., Kermarrec, A.-M., Guirguis, A., Riviere, M., & Tempo, R. (2020). FLEET: Flexible and Efficient Federated Learning for Edge AI. Proceedings of Machine Learning and Systems (MLSys).	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	OFL, federated online learning, streaming federated learning, real-time federated learning	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
Apparentées	5	3
Résumé	Online Federated Learning (OFL) combines the privacy-preserving, decentralised structure of federated learning with the sequential, sample-by-sample update regime of online learning. Clients — such as mobile devices or edge sensors — receive a global model, update it on newly arriving local data without sharing raw observations, and contribute compressed updates to a central server that aggregates them in near-real-time.	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.