Fed-GWAS: Privacy-preserving individualized incentive-based cross-device federated GWAS learning

Abstract

The widespread availability of DNA sequencing technology has led to the genetic sequences of individuals becoming accessible data, creating opportunities to identify the genetic factors underlying various diseases. In particular, Genome-Wide Association Studies (GWAS) seek to identify Single Nucleotide Polymorphism (SNPs) associated with a specific phenotype. Although sharing such data offers valuable insights, it poses a significant challenge due to both privacy concerns and the large size of the data involved. To address these challenges, in this paper, we propose a novel framework that combines both federated learning and blockchain as a platform for conducting GWAS studies with the participation of single individuals. The proposed framework offers a mutually beneficial solution where individuals participating in the GWAS study receive insurance credit to avail medical services while research and treatment centers benefit from the study data. To safeguard model parameters and prevent inference attacks, a secure aggregation protocol has been developed. The evaluation results demonstrate the scalability and efficiency of the proposed framework in terms of runtime and communication, outperforming existing solutions.