Machine learningMachine learning
在线联邦学习
在线联邦学习（Online Federated Learning, OFL）将联邦学习保护隐私的去中心化结构与在线学习的顺序式、逐样本更新机制相结合。客户端（如移动设备或边缘传感器）接收全局模型，在不共享原始观测数据的情况下，利用新到达的本地数据对其进行更新，并向中央服务器贡献压缩后的更新，服务器则近乎实时地聚合这些更新。
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来源
- Damaskinos, G., Guerraoui, R., Kermarrec, A.-M., Guirguis, A., Riviere, M., & Tempo, R. (2020). FLEET: Flexible and Efficient Federated Learning for Edge AI. Proceedings of Machine Learning and Systems (MLSys). link ↗
- McMahan, B., Moore, E., Ramage, D., Hampson, S., & Aguera y Arcas, B. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 54, 1273–1282. link ↗
如何引用本页
ScholarGate. (2026, June 3). Online Federated Learning (Sequential Distributed Learning without Centralised Data). ScholarGate. https://scholargate.app/zh/machine-learning/online-federated-learning
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