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| 자기 지도 연합 학습× | 퓨샷 학습× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2021–2022 | 2011–2017 |
| 창시자≠ | McMahan et al. (federated); Zhuang et al. and others (federated SSL combination) | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| 유형≠ | Federated self-supervised pretraining paradigm | Meta-learning / low-data learning paradigm |
| 원전≠ | Zhuang, W., Wen, Y., & Zhang, S. (2021). Divergence-aware Federated Self-Supervised Learning. In International Conference on Learning Representations (ICLR 2022). link ↗ | Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., & Kavukcuoglu, K. (2016). Matching Networks for One Shot Learning. Advances in Neural Information Processing Systems (NeurIPS), 29. link ↗ |
| 별칭 | FedSSL, Federated Self-supervised Learning, Federated Contrastive Learning, Self-supervised Federated Pretraining | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| 관련≠ | 5 | 4 |
| 요약≠ | 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. | Few-shot learning is a machine learning paradigm that trains models to recognize new classes or solve new tasks from only a handful of labeled examples — typically one to five — by leveraging prior knowledge acquired from a large, related training distribution. It is especially relevant in domains where labeling is expensive, scarce, or structurally limited. |
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