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新型NL-SME方法增强了联邦学习中的梯度反演攻击

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的NL-SME方法，旨在对抗联邦学习中的多步梯度反演攻击。该方法构建了一个可学习的非线性代理轨迹来近似隐藏的局部状态，将轨迹级别的信息与校准的梯度匹配相结合。NL-SME还包含一个更新可靠性感知策略，以减轻扰动更新中不可靠组件的影响。实验表明，NL-SME在重建质量和准确性方面显著优于现有方法，突显了即使在联邦学习的多步更新中也存在潜在的隐私泄露风险。 AI

影响 这项研究突显了联邦学习中潜在的隐私漏洞，促使对强大的防御机制进行进一步研究。

排序理由 该集群包含一篇学术论文，详细介绍了联邦学习中梯度反演攻击的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新型NL-SME方法增强了联邦学习中的梯度反演攻击

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该集群包含一篇学术论文，详细介绍了联邦学习中梯度反演攻击的新方法。[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) · Li Xia, Jing Yu, Zheng Liu, Sili Huang, Wei Tang, Xuan Liu ·

    面向联邦学习多步梯度反演的轨迹感知信息匹配

    arXiv:2509.22082v3 Announce Type: replace Abstract: Federated learning enables distributed information sharing and collaborative model training without exposing raw client data. However, shared gradients or model updates may still contain sensitive information, making federated l…

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