পদ্ধতির তুলনা করুন

	আধা-পর্যবেক্ষিত ফেডারেল লার্নিং	ট্রান্সফার লার্নিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2020	2010 (formalized); 1990s (early roots)
প্রবর্তক	Jeong, W. et al. / multiple independent groups	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
ধরন	Distributed semi-supervised learning framework	Learning paradigm
মৌলিক উৎস	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.
অপর নাম	SSL-FL, federated semi-supervised learning, FSSL, semi-supervised distributed learning	TL, domain adaptation, fine-tuning, pre-trained model adaptation
সম্পর্কিত	6	3
সারসংক্ষেপ	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.