{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computing, University of Portsmouth, Portsmouth, United Kingdom.",
    "School of Computing, Arden University, London, United Kingdom.",
    "School of Computing and Emerging Technologies, Roots IVY College of Management Sciences, Lahore, Pakistan.",
    "Faculty of Computing, Riphah International University, Islamabad, Pakistan.",
    "Department of Computing, Government Graduate College Women, Hasilpur, Pakistan.",
    "Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.",
    "Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si, Republic of Korea.",
    "Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0,
        1,
        2,
        3
      ],
      "name": "Muhammad Umar Nasir"
    },
    {
      "affs": [
        4
      ],
      "name": "Urva Rehmat"
    },
    {
      "affs": [
        5
      ],
      "name": "Abdul Khader Jilani Saudagar"
    },
    {
      "affs": [
        5
      ],
      "name": "Mona Alkhattabi"
    },
    {
      "affs": [
        6
      ],
      "name": "Muhammad Adnan Khan"
    },
    {
      "affs": [
        7
      ],
      "name": "Muhammad Amir Khan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3hya6xa9rxhmm.bib",
  "cited_by": 0,
  "doi": "10.3389/fmed.2026.1959427",
  "fulltext_url": "https://europepmc.org/articles/PMC13686847",
  "id": "3hya6xa9rxhmm",
  "issns": [
    "2296-858X"
  ],
  "landing_url": "https://europepmc.org/article/MED/42922839",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Privacy-Preserving Technologies in Data"
    ]
  ],
  "openalex": "W7220979768",
  "pdf_url": "https://europepmc.org/articles/PMC13686847?pdf=render",
  "pmcid": "PMC13686847",
  "pmid": "42922839",
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3hya6xa9rxhmm.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:33Z",
      "native_id": "MED:42922839",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42922839"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "journal-openalex:F27",
      "subfield": "general"
    }
  ],
  "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.",
  "title_text": "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.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:33Z",
  "url": "https://research.draconitic.com/paper/3hya6xa9rxhmm",
  "venue": "Frontiers in medicine",
  "versions": []
}