Federated Fairness-Aware Learning Framework for Predicting Antiretroviral Therapy Outcomes Across Multi-Institutional Electronic Health Records in Underserved Populations

Abstract

Antiretroviral therapy has transformed HIV infection into a manageable chronic condition, yet treatment outcomes remain uneven across demographic and socioeconomic strata, especially in underserved communities where electronic health record data are dispersed across multiple institutions. The rising adoption of machine learning for clinical outcome prediction presents both an opportunity and a structural challenge: predictive models must learn from diverse, multi-institutional data without centralizing sensitive patient information, while simultaneously mitigating disparities that arise from historically biased datasets. This paper proposes a federated fairness-aware learning framework for predicting antiretroviral therapy outcomes across multi-site electronic health records, with explicit design attention to populations experiencing systemic marginalization. We articulate a system-level architecture that couples cross-silo federated learning with fairness constraints, enabling institutions to collaboratively train a global model without exposing individual-level records. The framework addresses longitudinal missingness, demographic skew, site-specific data heterogeneity, and algorithmic fairness through a combination of local debiasing, global fairness aggregation, and continuous fairness auditing. We examine the structural trade-offs among privacy preservation, model performance, and equity objectives, highlighting tensions between local optimization and global fairness. The discussion extends to the governance infrastructure necessary for sustaining such a learning network, including institutional agreements, computational resource allocation, and policy alignment with health equity mandates. By integrating technical design with organizational and ethical considerations, this work offers a holistic blueprint for deploying equitable predictive systems in HIV care. The framework is not merely a technical artefact but a socio-technical intervention that can reshape how health systems learn from distributed data while centering the experiences of populations historically excluded from biomedical research.