השוואת שיטות

	למידה פדרטיבית מקוונת	למידה מקוונת
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2019–2021	1958–2000s
הוגה השיטה	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)
סוג	Distributed sequential learning	Learning paradigm (sequential model update)
מקור מכונן	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.
כינויים	OFL, federated online learning, streaming federated learning, real-time federated learning	incremental learning, sequential learning, streaming learning, online machine learning
קשורות	5	6
תקציר	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.