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English(EN) FL-Sailer: Efficient and Privacy-Preserving Federated Learning for Scalable Single-Cell Epigenetic Data Analysis via Adaptive Sampling

FL-Sailer框架为表观基因组数据实现注重隐私的联邦学习

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了FL-Sailer，一个专门用于分析单细胞ATAC-seq数据的新型联邦学习框架。该框架通过引入自适应杠杆分数采样（将维度降低80%）和不变VAE架构（将生物信号与技术噪声分离）来解决高维度和数据异质性等挑战。FL-Sailer不仅促进了多机构协作并保护隐私，而且与集中式方法相比，其性能也更优越。 AI

影响 通过克服单细胞分析中的隐私和数据大小限制，实现了协作式表观基因组学研究。

排序理由 这是一篇详细介绍新数据分析框架的研究论文。

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

FL-Sailer框架为表观基因组数据实现注重隐私的联邦学习

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Guangyi Zhang, Yi Dai, Yiyun He, Junhao Liu ·

    FL-Sailer：用于可扩展单细胞表观遗传数据分析的高效且注重隐私的联邦学习，通过自适应采样实现

    arXiv:2605.04519v1 Announce Type: new Abstract: Single-cell ATAC-seq (scATAC-seq) enables high-resolution mapping of chromatin accessibility, yet privacy regulations and data size constraints hinder multi-institutional sharing. Federated learning (FL) offers a privacy-preserving …

  2. arXiv stat.ML TIER_1 English(EN) · Junhao Liu ·

    FL-Sailer：用于可扩展单细胞表观遗传数据分析的高效且注重隐私的联邦学习，通过自适应采样实现

    Single-cell ATAC-seq (scATAC-seq) enables high-resolution mapping of chromatin accessibility, yet privacy regulations and data size constraints hinder multi-institutional sharing. Federated learning (FL) offers a privacy-preserving alternative, but faces three fundamental barrier…

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