ArticleJAMIA open2026
Privacy-preserving verification of preprocessing in federated learning for genomic data.
Article in JAMIA open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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Abstract
Objectives: To verify that federated genomic study sites applied identical preprocessing pipelines without disclosing raw genotypes. Materials and Methods: Each institution perturbs a 100-SNP slice using local differential privacy (LDP), trains a RandomForest classifier, and transmits one LIME explanation vector to a coordinating server. The server simulates 15 preprocessing combinations and trains a RandomForest classifier to predict each site's configuration. Results: In centralized simulation, the verifier achieved 80% accuracy across 15 preprocessing configurations on the GMMAT ( Discussion: A single differentially private explanation vector provides an auditable preprocessing fingerprint. The gap between centralized and distributed accuracy reflects expected FL data partitioning effects. Conclusion: This framework demonstrates the feasibility of automated preprocessing verification in federated genomic consortia without compromising participant privacy.
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