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 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 eq
The increasing sensitivity around health data privacy and cross-border data transfer limitations are driving the need for federated learning solutions in biomedical research.
This development addresses a critical bottleneck in leveraging large, diverse clinical datasets for AI model training, particularly in sensitive domains like genomics and transcriptomics.
The ability to train robust AI models on distributed, private clinical data without direct data pooling will accelerate drug discovery, personalized medicine, and biomarker identification.
- · Pharmaceutical companies
- · Biotech firms
- · Healthcare AI developers
- · Patients with complex diseases
- · Traditional data aggregators
- · Researchers reliant on single-source data
Federated learning becomes a standard paradigm for AI development in regulated and privacy-sensitive sectors like healthcare.
Accelerated discovery of novel drug targets and diagnostic biomarkers due to the ability to analyze previously siloed clinical data.
The development of a global network for collaborative biomedical AI research, transcending national data sovereignty boundaries through secure computation methods.
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Read at arXiv cs.LG