Vertaile menetelmiä

	Puolivalvottu hajautettu oppiminen	Hajautettu oppiminen
Tieteenala	Koneoppiminen	Yksityisyydensuoja
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2020	2017
Kehittäjä	Jeong, W. et al. / multiple independent groups	McMahan et al.
Tyyppi	Distributed semi-supervised learning framework	Distributed privacy-preserving machine learning
Alkuperäislähde	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).	McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics, 1273–1282.
Rinnakkaisnimet	SSL-FL, federated semi-supervised learning, FSSL, semi-supervised distributed learning	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
Liittyvät	6	3
Tiivistelmä	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.	Federated Learning is a distributed machine learning paradigm introduced by McMahan et al. in 2017 in which a global model is trained collaboratively across multiple decentralized clients — such as mobile devices or hospital systems — without ever transferring raw data to a central server. Each participant computes model updates locally using its private data; only those updates, not the underlying data, are communicated and aggregated by the server to improve the shared model.