Federated Learning for Privacy-Preserving Immune Gene Typing and Cross-Cohort Immunogenomic Analysis from Long-Read Sequencing
Keywords:
federated learning, privacy-preserving genomics, immune gene typing, long-read sequencing, cross-cohort analysis, differential privacy, secure aggregation, immunogenomics, data governanceAbstract
The rapid adoption of long-read sequencing technologies has enabled high-resolution typing of highly polymorphic immune genes, such as those in the major histocompatibility complex, yet the aggregation of such data across multiple cohorts for immunogenomic association studies introduces significant privacy risks. This paper proposes a federated learning framework designed to enable privacy-preserving immune gene typing and cross-cohort immunogenomic analysis from distributed long-read sequencing datasets. We conceptualize a system architecture that integrates local model training on cohort-specific sequencing repositories with secure aggregation protocols, differential privacy mechanisms, and decentralized governance structures. The framework addresses critical trade-offs between model fidelity, communication efficiency, statistical power, and protection against re-identification attacks. We examine the infrastructural demands of deploying such a system across heterogeneous clinical and research sites, including the need for harmonized variant calling pipelines, standardized immune gene annotations, and robust quality control measures that preserve privacy while ensuring biological validity. Furthermore, we analyze the governance and policy implications of federated immunogenomic analysis, including consent management, data sovereignty, and equitable access to derived models. By drawing parallels to existing federated learning deployments in medical imaging and electronic health records, we discuss sustainability, fairness, and robustness challenges specific to polymorphic gene typing. Our analysis concludes that while federated learning offers a compelling paradigm for multi-cohort immunogenomic discovery, its successful implementation requires careful orchestration of algorithmic, regulatory, and ethical dimensions.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
