Bandingkan metode
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| Pembelajaran Federasi Mandiri (Self-supervised Federated Learning)× | Pembelajaran Semi-terawasi× | |
|---|---|---|
| Bidang | Pembelajaran Mesin | Pembelajaran Mesin |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 2021–2022 | 1970s–2006 (formalized) |
| Pencetus≠ | McMahan et al. (federated); Zhuang et al. and others (federated SSL combination) | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Tipe≠ | Federated self-supervised pretraining paradigm | Learning paradigm |
| Sumber perintis≠ | Zhuang, W., Wen, Y., & Zhang, S. (2021). Divergence-aware Federated Self-Supervised Learning. In International Conference on Learning Representations (ICLR 2022). link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Alias | FedSSL, Federated Self-supervised Learning, Federated Contrastive Learning, Self-supervised Federated Pretraining | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Terkait | 5 | 5 |
| Ringkasan≠ | 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. | Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained. |
| ScholarGateSet data ↗ |
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