{"paper_id":"44e37c7a-84bb-4eee-a566-248effeb363f","body_text":"Abstract\nBackground Predictive modeling in healthcare holds promise for improving clinical outcomes, but in many low-resource settings, data fragmentation, privacy concerns, and infrastructural limitations hinder centralized machine learning approaches. This is particularly relevant in HIV care, where accurately identifying patients at risk of viral load (VL) non-suppression is essential for timely intervention.\nMethods We developed a privacy-preserving federated learning (FL) framework to predict HIV VL suppression using retrospective data from 50,000 patients and over one million visits across 30 health facilities in Uganda. The framework utilizes federated averaging for distributed training, secure multiparty aggregation, and differential privacy to ensure data confidentiality. To address cross-site heterogeneity, we integrated domain-adversarial neural networks to promote domain-invariant feature learning. A multilayer perceptron model was trained collaboratively across facilities using local data only.\nResults The federated model achieved an area under the ROC curve (AUC) of 0.874, nearly matching a centralized baseline (AUC 0.881) and substantially outperforming site-specific models (average AUC 0.758). Sensitivity (89.6%) and specificity (66.8%) demonstrate strong capability in identifying both suppressed and unsuppressed cases. Domain adaptation reduced inter-facility performance variability, and differential privacy imposed minimal accuracy degradation. Training was completed within one hour using modest hardware, which supported feasibility in low-resource settings.\nConclusion Our study demonstrates that FL can deliver robust, privacy-preserving predictive performance in HIV care without centralizing sensitive patient data. The proposed architecture is adaptable to other clinical prediction tasks and represents a practical pathway for scaling ethical AI across decentralized healthcare systems in low- and middle-income countries.\nCompeting Interest Statement\nThe authors have declared no competing interest.\nFunding Statement\nNo funding was received for this study.\nAuthor Declarations\nI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.\nYes\nThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:\nOnly de-identified secondary data were used.\nI confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.\nYes\nI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).\nYes\nI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.\nYes","source_license":"CC-BY-4.0","license_restricted":false}