方法证据记录
Self-supervised Federated learning
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Self-supervised Learning in Federated Settings
分类方法记录 · ml-model / machine-learning
- Zhuang, W., Wen, Y., & Zhang, S. (2021). Divergence-aware Federated Self-Supervised Learning. In International Conference on Learning Representations (ICLR 2022). · URL
- Federated learning. Wikipedia. · URL
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