Compară metode

	Învățare federativă semi-supervizată	Învățare prin transfer
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2020	2010 (formalized); 1990s (early roots)
Autorul original	Jeong, W. et al. / multiple independent groups	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Tip	Distributed semi-supervised learning framework	Learning paradigm
Sursa seminală	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).	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Denumiri alternative	SSL-FL, federated semi-supervised learning, FSSL, semi-supervised distributed learning	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Înrudite	6	3
Rezumat	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.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.