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.
8 affiliations
- 1School of Computing, University of Portsmouth, Portsmouth, United Kingdom.
- 2School of Computing, Arden University, London, United Kingdom.
- 3School of Computing and Emerging Technologies, Roots IVY College of Management Sciences, Lahore, Pakistan.
- 4Faculty of Computing, Riphah International University, Islamabad, Pakistan.
- 5Department of Computing, Government Graduate College Women, Hasilpur, Pakistan.
- 6Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
- 7Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si, Republic of Korea.
- 8Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia.
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.
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BibTeX
@article{nasir2026fftgabc,
title = {{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.}},
author = {Muhammad Umar Nasir and Urva Rehmat and Abdul Khader Jilani Saudagar and Mona Alkhattabi and Muhammad Adnan Khan and Muhammad Amir Khan},
journal = {Frontiers in medicine},
year = {2026},
month = oct,
doi = {10.3389/fmed.2026.1959427},
url = {https://europepmc.org/article/MED/42922839},
note = {CC BY},
}
RIS
TY - JOUR
TI - 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.
AU - Muhammad Umar Nasir
AU - Urva Rehmat
AU - Abdul Khader Jilani Saudagar
AU - Mona Alkhattabi
AU - Muhammad Adnan Khan
AU - Muhammad Amir Khan
PY - 2026
DA - 2026/10/08
JO - Frontiers in medicine
DO - 10.3389/fmed.2026.1959427
UR - https://europepmc.org/article/MED/42922839
AB - 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.
SN - 2296-858X
ER -