FFT-GA-BC: a fused federated transformer genetic algorithm framework with blockchain-secured, differentially private, homomorphically encrypted aggregation for mutation, phenotype, and binary-allele analysis

Abstract

Federated learning enables cross-institutional clinical-genetics analytics without sharing raw patient data; yet, no existing framework integrates the full cryptographic stack–differential privacy, homomorphic encryption, and blockchain auditing—required for regulatory deployment. Here, we present FFT-GA-BC, a secure-by-construction pipeline combining a bit-flip genetic algorithm for binary feature selection across 136-dimensional genomic representations, a multi-head self-attention encoder, population-stratified federated averaging, ( ε , δ )-differential privacy via the analytic Gaussian mechanism, Cheon–Kim–Kim–Song homomorphic encryption for cipher-domain weight aggregation, and a Practical Byzantine Fault Tolerance (PBFT)-consensus blockchain with SHA-256 Merkle-root commitments per federation round. We evaluate the framework under a simulated multi-institutional deployment scenario using a 950-record synthetic benchmark whose schema mirrors the Punjab Thalassemia and Genetic Disorders Programme Lahore registry, a South Asian clinical-genetics registry operating across six ancestry strata federated across a simulated 12-hospital, five-continent topology, and internally externally validated via a leave-one-population-out proxy across all six ancestry cohorts. Under these conditions, differential privacy imposes less than a 2.7 percentage-point utility loss across the clinically relevant privacy-budget range, with ε = 1 recommended as the clinical operating point (Section 5.8 ), and fully encrypted federated training matches centralised pooled training within ±1.6 percentage points across five independent splits. The blockchain audit layer scales to 192 nodes at 1.3 s of per-round latency with on-chain audit costs below USD 350 per block, maintaining an O (log K ) Merkle-inclusion proof complexity per query. Information-theoretic profiling of the benchmark confirms that labels carry at most 0.036 bits of mutual information against a target entropy of 2.0 bits, demonstrating that the pipeline's diagnostic output faithfully reflects data structure rather than architectural artefacts, a critical validation property for clinical deployment. To the best of our knowledge, based on a systematic literature search (PubMed, IEEE Xplore, arXiv; terms: federated learning + genomics + privacy + blockchain; June 2026), FFT-GA-BC provides the first fully specified, reproducible, and externally validated end-to-end secure federated genomics pipeline architecturally and infrastructurally ready for multi-hospital deployment, pending real-cohort clinical validation.