Salīdzināt metodes

	Pašuzraudzītā federatīvā apmācība	Federatīvā apmācība
Nozare	Mašīnmācīšanās	Privātums
Saime	Machine learning	Machine learning
Izcelsmes gads	2021–2022	2017
Autors	McMahan et al. (federated); Zhuang et al. and others (federated SSL combination)	McMahan et al.
Tips	Federated self-supervised pretraining paradigm	Distributed privacy-preserving machine learning
Pirmavots	Zhuang, W., Wen, Y., & Zhang, S. (2021). Divergence-aware Federated Self-Supervised Learning. In International Conference on Learning Representations (ICLR 2022).	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.
Citi nosaukumi	FedSSL, Federated Self-supervised Learning, Federated Contrastive Learning, Self-supervised Federated Pretraining	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
Saistītās	5	3
Kopsavilkums	Self-supervised Federated Learning combines federated training — where data never leaves local devices — with self-supervised pretext tasks such as contrastive learning or masked prediction. Clients learn general-purpose representations from their own unlabeled data and share only model updates, not raw data, with a central server that aggregates them into a global encoder.	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.