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	स्व-पर्यवेक्षित संघटित शिक्षण (Self-supervised Federated Learning)	ट्रांसफर लर्निंग
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2021–2022	2010 (formalized); 1990s (early roots)
प्रवर्तक	McMahan et al. (federated); Zhuang et al. and others (federated SSL combination)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
प्रकार	Federated self-supervised pretraining paradigm	Learning paradigm
मौलिक स्रोत	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.
उपनाम	FedSSL, Federated Self-supervised Learning, Federated Contrastive Learning, Self-supervised Federated Pretraining	TL, domain adaptation, fine-tuning, pre-trained model adaptation
संबंधित	5	3
सारांश	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.