Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Федеративное обучение с самоконтролем (Self-supervised Federated Learning)× | Перенос обучения× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2021–2022 | 2010 (formalized); 1990s (early roots) |
| Автор метода≠ | McMahan et al. (federated); Zhuang et al. and others (federated SSL combination) | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Тип≠ | Federated self-supervised pretraining paradigm | Learning paradigm |
| Основополагающий источник≠ | Zhuang, W., Wen, Y., & Zhang, S. (2021). Divergence-aware Federated Self-Supervised Learning. In International Conference on Learning Representations (ICLR 2022). link ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| Другие названия | FedSSL, Federated Self-supervised Learning, Federated Contrastive Learning, Self-supervised Federated Pretraining | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Связанные≠ | 5 | 3 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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