Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Kujifundisha kwa Kujitegemea kwa Njia ya Shirikishi× | Kujifunza kwa uhamishaji× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Mashine | Ujifunzaji wa Mashine |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2021–2022 | 2010 (formalized); 1990s (early roots) |
| Mwanzilishi≠ | McMahan et al. (federated); Zhuang et al. and others (federated SSL combination) | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Aina≠ | Federated self-supervised pretraining paradigm | Learning paradigm |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala | FedSSL, Federated Self-supervised Learning, Federated Contrastive Learning, Self-supervised Federated Pretraining | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Zinazohusiana≠ | 5 | 3 |
| Muhtasari≠ | 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. |
| ScholarGateSeti ya data ↗ |
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