DFDG: Adaptive federated learning for dynamic graph-based traffic forecasting

Muhammad Usman

doi:10.1016/j.knosys.2025.114019

2025-06-30

PRE

Muhammad Usman

Yugyung Lee

DOI：10.1016/j.knosys.2025.114019

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摘要

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• 提出DFDG，一种集成动态GNNs、KANODE和隐私保护傅里叶编码的联邦框架。 • 在三个真实交通数据集上，相较于先前的算法，误差降低了高达18%。 • 推理时间低于3秒，并可扩展至21个异步参与者。 • 消融研究证实了时序建模和自适应聚合的重要性。 • 代码和评估工具将在稿件录用后发布。

期刊

Knowledge-Based Systems

IF:

7.6

论文数:

1.3W

被引数:

4.5W

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