Σύγκριση μεθόδων

	Online Federated Learning	Διαδικτυακή Μάθηση
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2019–2021	1958–2000s
Δημιουργός	McMahan, B. et al. (FL foundation); extended to online setting by multiple researchers c. 2019–2021	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Τύπος	Distributed sequential learning	Learning paradigm (sequential model update)
Θεμελιώδης πηγή	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).	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Εναλλακτικές ονομασίες	OFL, federated online learning, streaming federated learning, real-time federated learning	incremental learning, sequential learning, streaming learning, online machine learning
Συναφείς	5	6
Σύνοψη	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.	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.