The Antibody Mediated Prevention (AMP) trials opened a new scientific frontier by showing that passively administered monoclonal broadly neutralizing antibodies (bnAbs) could prevent HIV-1 acquisition. Conducted across multiple geographic regions, including the United States, Brazil, Peru, Switzerland, and sub-Saharan Africa, the AMP trials revealed substantial regional heterogeneity in treatment efficacy. These differences, together with privacy and regulatory limits on central data pooling, call for methods that borrow strength across regions without sharing individual-level data. To estimate region- and treatment-specific survival curves under distributional heterogeneity, we develop a federated learning approach that combines site-specific estimators via an L1-regularized criterion that downweights data sources not aligned with the target. We further extend the framework to a general class of causal contrasts, including the risk difference (RD), survival ratio (SR), and restricted mean survival time (RMST) difference. Through extensive simulations and an analysis of the AMP trials under different target populations, we show that the proposed approach provides privacy-preserving, region-adaptive inference with improved precision.

翻译：抗体介导预防（AMP）试验开辟了全新的科学领域，证明被动施用的单克隆广谱中和抗体（bnAbs）能够预防HIV-1病毒感染。该试验涵盖美国、巴西、秘鲁、瑞士及撒哈拉以南非洲等多个地理区域，揭示了治疗效果的显著区域异质性。这些差异，加之中央数据池化面临的隐私与监管限制，亟需无需共享个体级数据即可跨区域借力强化的分析方法。为在分布异质性条件下估计区域及治疗特异性生存曲线，我们开发了一种联邦学习框架，通过L1正则化准则整合各站点特异性估计量，对与目标区域不一致的数据源进行降权处理。我们进一步将该框架扩展至包含风险差（RD）、生存比（SR）及限制性平均生存时间（RMST）差值的广泛因果对比类别。通过大规模模拟实验及对不同目标人群的AMP试验分析，我们证明所提方法能够实现隐私保护下的区域自适应推断，并显著提升估计精度。