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English(EN) PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning

PRISM框架解决了联邦图学习中的模态缺失问题

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出PRISM，一个新颖的联邦图学习框架，用于解决跨不同客户端的模态缺失挑战。PRISM能够从包含文本和图像的去中心化图中进行协作学习，即使单个客户端缺乏完整的多模态数据。该框架通过拓扑感知控制，主动地从联邦中检索并填充缺失的模态语义，并将其整合到本地图传播中。实验证明了PRISM的有效性，在六个多模态图数据集上，平均比最先进的基线提高了4.48%。 AI

影响 增强了从去中心化多模态数据中进行协作学习的能力，可能改进依赖于多样化数据源的AI应用。

排序理由 该集群包含一篇详细介绍新框架和实验结果的研究论文。

在 arXiv cs.LG 阅读 →

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PRISM框架解决了联邦图学习中的模态缺失问题

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Zekai Chen, Miao Zhang, Jiayang Xing, Xunkai Li, Xun Wu, Rong-Hua Li, Guoren Wang ·

    PRISM：面向模态缺失的联邦图学习的拓扑感知跨模态填充

    arXiv:2606.09301v1 Announce Type: new Abstract: Multimodal federated graph learning (MM-FGL) aims to collaboratively learn from decentralized graphs with text and images. However, real-world clients may not share a common modality basis: a visual-search client may contain image--…

  2. arXiv cs.LG TIER_1 English(EN) · Guoren Wang ·

    PRISM：面向模态缺失的联邦图学习的拓扑感知跨模态插补

    Multimodal federated graph learning (MM-FGL) aims to collaboratively learn from decentralized graphs with text and images. However, real-world clients may not share a common modality basis: a visual-search client may contain image--interaction graphs but no seller descriptions, w…

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