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English(EN) F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting

新的F2STNet框架通过公平性增强了联邦图预测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的联邦学习框架F$^2$STNet，用于图结构时空预测。该模型集成了谱图-傅里叶特征、线性复杂度的状态空间时间编码器和图卷积层。为了解决去中心化环境中的数据异质性问题，F$^2$STNet采用了公平感知联邦聚合（FFA）机制，该机制改进了标准的FedAvg方法。在PeMS04、HZMetro和KnowAir等数据集上的实验表明，在联邦设置下预测精度和公平性得到了提高。 AI

影响 引入了一种用于时空图预测的联邦学习新方法，有望改进去中心化数据建模。

排序理由 该集群包含一篇详细介绍新模型和方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的F2STNet框架通过公平性增强了联邦图预测

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该集群包含一篇详细介绍新模型和方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jiayi Zhang, Jinfeng Xu, Hewei Wang, Siyuan Cen, Haidong Huang, Yiyao Zhan, Zheyu Chen, Jinjiang You, Ai Jian, Edith C. H. Ngai ·

    F2STNet：图预测的公平和联邦谱时建模

    arXiv:2608.09082v1 Announce Type: new Abstract: Spatiotemporal prediction on graph-structured data is central to traffic forecasting and environmental monitoring, yet decentralized and heterogeneous data complicate both sequence modeling and collaborative training. We propose F$^…

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