Privacy-preserving verification of preprocessing in federated learning for genomic data

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 ( n = 400) and synthetic genome ( n = 2504) datasets while maintaining membership-inference attack power below 0.05 at ε = 3. In distributed Flower FL experiments with data partitioned across three sites, binary compatibility detection reached 70% accuracy at 500 SNPs. 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.

Abstract of "Privacy-preserving verification of preprocessing in federated learning for genomic data" by Li W, Halimi A, Vaidya J et al., https://doi.org/10.1093/jamiaopen/ooag040, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.