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Self-supervised Federated Learning Self-supervised Federated Learning 结合了联邦训练（数据从不离开本地设备）与自监督的借口任务（如对比学习或掩码预测）。客户端从各自的无标签数据中学习通用表示，并且仅与聚合它们的全局编码器的中央服务器共享模型更新，而非原始数据。
速览
Originator McMahan et al. (federated); Zhuang et al. and others (federated SSL combination)
Year 2021–2022
Type Federated self-supervised pretraining paradigm
DataType Unlabeled distributed data (images, text, sensor readings)
Subfamily Machine learning 本页目录
Method map The neighbourhood of related methods — select a node to explore.
来源 Zhuang, W., Wen, Y., & Zhang, S. (2021). Divergence-aware Federated Self-Supervised Learning. In International Conference on Learning Representations (ICLR 2022). link ↗ Federated learning. Wikipedia. link ↗ 如何引用本页 APA BibTeX RIS 复制
ScholarGate. (2026, June 3). Self-supervised Learning in Federated Settings. ScholarGate. https://scholargate.app/zh/machine-learning/self-supervised-federated-learning
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Which method? Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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ScholarGate — Self-supervised Federated learning (Self-supervised Learning in Federated Settings). 于 2026-06-15 检索自 https://scholargate.app/zh/machine-learning/self-supervised-federated-learning · 数据集： https://doi.org/10.5281/zenodo.20539026