So sánh phương pháp

	Học Liên kết Bán Giám sát	Học tăng cường tự giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2020	2018–2020
Người khởi xướng	Jeong, W. et al. / multiple independent groups	LeCun, Y. and community (formalized ~2018–2020)
Loại	Distributed semi-supervised learning framework	Representation learning paradigm
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).	LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/
Tên gọi khác	SSL-FL, federated semi-supervised learning, FSSL, semi-supervised distributed learning	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Liên quan	6	3
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.	Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples.