新框架增强了联邦图神经网络的隐私性和效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-25 18:10

研究人员开发了一个名为CE-FedGNN的新框架，用于在分布式数据集上训练图神经网络（GNN）。该方法解决了在关系数据上进行联邦学习时与隐私和通信成本相关的挑战。CE-FedGNN通过不频繁地交换聚合表示来最小化共享原始数据或节点嵌入的需求，同时还结合了差分隐私来保护发布的信息。 AI

影响这项研究为在分布式数据上训练GNN提供了一种更有效且注重隐私的方法，有可能在新兴的无法集中数据的应用中发挥作用。

排序理由该集群包含一篇详细介绍图神经网络新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍图神经网络新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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Hugging Face Daily Papers TIER_1 English(EN) · 2026-05-25 18:10

可证明通信高效且隐私保护的联邦图神经网络

Graph neural networks (GNNs) achieve strong performance on relational data, but real-world graphs are often distributed across organizations that cannot share raw data due to privacy and policy constraints. Existing federated GNN methods either ignore cross-client links, leading …