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English(EN) Evolving Federated Density Peak Clustering with Shuffler-Based Anonymization

新的联邦聚类方法增强了隐私和效率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的单次联邦聚类方法，称为演进式联邦密度峰值聚类（eFDPC）。该方法将演进式模糊数据流聚类与密度峰值聚类相结合，在联邦环境中进行，允许客户端从私有数据中独立演进微聚类。为了最小化通信，只有选定的峰值会被传输到服务器进行全局选择。集成了一个基于洗牌器的层，用于在重构之前匿名化客户端的贡献，从而增强隐私。实验表明，eFDPC在单次场景下优于现有的联邦聚类基线，并能有效处理高维、非独立同分布数据。 AI

影响 这种新的联邦聚类方法可以提高分布式机器学习任务中的隐私和效率。

排序理由 该条目是一篇学术论文，详细介绍了一种新的联邦聚类方法。[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 →

新的联邦聚类方法增强了隐私和效率

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该条目是一篇学术论文，详细介绍了一种新的联邦聚类方法。[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) · Miha O\v{z}bot, Seiichi Ozawa, Igor \v{S}krjanc ·

    Evolving Federated Density Peak Clustering with Shuffler-Based Anonymization

    arXiv:2610.31211v1 Announce Type: new Abstract: In this paper, we present a novel one-shot federated clustering approach based on evolving fuzzy clustering and shuffler-based anonymization. In federated clustering, each round reveals additional model information to the server and…

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