So sánh phương pháp

	Học Liên kết Bán Giám sát	Học Liên kết Trực tuyến
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2020	2019–2021
Người khởi xướng	Jeong, W. et al. / multiple independent groups	McMahan, B. et al. (FL foundation); extended to online setting by multiple researchers c. 2019–2021
Loại	Distributed semi-supervised learning framework	Distributed sequential learning
Công trình gốc	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).
Tên gọi khác	SSL-FL, federated semi-supervised learning, FSSL, semi-supervised distributed learning	OFL, federated online learning, streaming federated learning, real-time federated learning
Liên quan	6	5
Tóm tắt	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.