Salīdzināt metodes

	Tiešsaistes apguve	Federatīvā apmācība
Nozare	Mašīnmācīšanās	Privātums
Saime	Machine learning	Machine learning
Izcelsmes gads	1958–2000s	2017
Autors	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)	McMahan et al.
Tips	Learning paradigm (sequential model update)	Distributed privacy-preserving machine learning
Pirmavots	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.	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.
Citi nosaukumi	incremental learning, sequential learning, streaming learning, online machine learning	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
Saistītās	6	3
Kopsavilkums	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.	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.