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Federated Learning Enhances Transcriptomics Privacy with FPLIER

By PulseAugur Editorial · [1 sources] ·

Researchers have developed FPLIER, a federated learning approach that extends the Pathway Level Information Extractor (PLIER) for transcriptomics. This method allows for distributed training of gene-set-aware factorization models across multiple data holders without centralizing sensitive expression data. FPLIER uses secure aggregation to produce training updates equivalent to a centralized approach, while also analyzing privacy risks related to membership inference attacks. The study demonstrates stable convergence and shows that privacy is enhanced by incorporating public data or reducing data dimensionality. AI

RANK_REASON The cluster contains a research paper detailing a new method for transcriptomics analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Federated Learning Enhances Transcriptomics Privacy with FPLIER

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The cluster contains a research paper detailing a new method for transcriptomics analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Daniele Malpetti, Christian Berchtold, Francesco Gualdi, Marco Scutari, Laura Azzimonti, Francesca Mangili ·

    FPLIER: Federated Pathway-Level Information Extractor

    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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