Salīdzināt metodes

	Puss-uzraudzīta federatīvā apmācība	Tiešsaistes federatīvā apmācība
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2020	2019–2021
Autors	Jeong, W. et al. / multiple independent groups	McMahan, B. et al. (FL foundation); extended to online setting by multiple researchers c. 2019–2021
Tips	Distributed semi-supervised learning framework	Distributed sequential learning
Pirmavots	Jeong, W., Yoon, J., Yang, E., & Hwang, S. J. (2020). Federated Semi-Supervised Learning with Inter-Client Consistency. International Conference on Learning Representations (ICLR 2021).	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).
Citi nosaukumi	SSL-FL, federated semi-supervised learning, FSSL, semi-supervised distributed learning	OFL, federated online learning, streaming federated learning, real-time federated learning
Saistītās	6	5
Kopsavilkums	Semi-supervised federated learning (SSFL) trains a shared model across many decentralized clients — each holding private data — when only a subset of clients or a subset of local samples carry labels. It combines the privacy-preserving coordination of federated learning with the label-efficiency of semi-supervised techniques such as pseudo-labeling and consistency regularization, enabling strong model quality without centralizing sensitive data.	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.