Compară metode

	Învățare federată auto-supervizată	Învățare prin transfer
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2021–2022	2010 (formalized); 1990s (early roots)
Autorul original	McMahan et al. (federated); Zhuang et al. and others (federated SSL combination)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Tip	Federated self-supervised pretraining paradigm	Learning paradigm
Sursa seminală	Zhuang, W., Wen, Y., & Zhang, S. (2021). Divergence-aware Federated Self-Supervised Learning. In International Conference on Learning Representations (ICLR 2022).	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Denumiri alternative	FedSSL, Federated Self-supervised Learning, Federated Contrastive Learning, Self-supervised Federated Pretraining	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Înrudite	5	3
Rezumat	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.	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.