FPLIER: Federated Pathway-Level Information Extractor
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 cannot be pooled into a single dataset due to privacy and governance constraints. We present FPLIER, a federated extension of PLIER that enables distributed training across multiple data holders while incorporating publicly available datasets. Through secure aggregation, FPLIER produces training updates algebraically equivalent to those of a centralized pooled-data approach while keeping expression data local. We evaluate FPLIER across multiple scenarios in two si...
| Authors | Daniele Malpetti, Christian Berchtold, Francesco Gualdi et al. |
|---|---|
| Primary category | q-bio.QM |
| Published | 28 May 2026 |
| Last updated at source | 28 May 2026 |
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