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English(EN) FPLIER: Federated Pathway-Level Information Extractor

联邦学习通过FPLIER增强转录组学的隐私性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了FPLIER，一种联邦学习方法，它扩展了用于转录组学的通路级别信息提取器（PLIER）。该方法允许在不集中敏感表达数据的情况下，跨多个数据持有者对基因集感知的因子分解模型进行分布式训练。FPLIER使用安全聚合来产生相当于集中式方法的训练更新，同时还分析与成员推断攻击相关的隐私风险。研究表明，通过整合公共数据或降低数据维度，可以实现稳定的收敛并增强隐私性。 AI

排序理由 该集群包含一篇详细介绍转录组学分析新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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联邦学习通过FPLIER增强转录组学的隐私性

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该集群包含一篇详细介绍转录组学分析新方法的学术论文。[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) · Daniele Malpetti, Christian Berchtold, Francesco Gualdi, Marco Scutari, Laura Azzimonti, Francesca Mangili ·

    FPLIER: 联邦路径级信息提取器

    arXiv:2605.29587v1 Announce Type: cross Abstract: In transcriptomics, gene-set-aware factorization methods such as the Pathway Level Information Extractor (PLIER) are most effective when trained on large, heterogeneous expression compendia. Yet, many clinically relevant cohorts c…

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