AI 中文总结
该研究针对跨机构联合分析的隐私与效率问题，提出基于三阶泰勒展开的单通信联邦推理算法，提升小样本场景下对数似然近似准确性，兼顾隐私、通信效率与可扩展性。
AI 中文摘要
在生物医学和流行病学研究中，跨多个机构的联合分析日益重要，尤其是针对数据集通常较小的罕见病。然而，隐私法规和机构政策常常禁止共享个体层面的患者数据。本文提出一种准确的单通信联邦推理算法。单通信联邦推理通过参与中心与协调服务器之间仅交换一次汇总统计量即可实现统计分析，相比迭代联邦学习，它在保护隐私的同时降低了通信和计算成本。我们扩展了最近提出的基于二阶泰勒展开的单通信联邦推理策略，采用三阶展开来更好地近似局部对数似然函数。通过基于真实数据的模拟研究对所提方法进行评估，并与现有联邦推理策略比较。该模拟研究评估了所提方法的性能，尤其关注局部样本量较小的场景，在此场景中二次近似可能无法捕捉对数似然函数的偏度及其他高阶特征。结果表明，纳入对数似然函数的高阶信息可提升准确性，同时保留协作生物医学和流行病学研究所需的隐私保护、通信效率及可扩展性。
英文摘要
Joint analyses across multiple institutions are increasingly important in biomedical and epidemiological research, particularly for rare diseases where datasets are typical small. However, privacy regulations and institutional policies often prevent the sharing of individual-level patient data. In this paper we present an accurate and single-communication federated inference algorithm. Single-communication federated inference enables statistical analyses through a single exchange of summary statistics between participating centers and a coordinating server, preserving privacy while reducing communication and computational costs compared with iterative federated learning. We extend a recently proposed single-communication federated inference strategy that is based on second-order Taylor expansions by using third-order expansions to better approximate local log-likelihood functions. The proposed method is evaluated through simulation studies based on real data and compared with existing federated inference strategies. The simulation studies assess the performance of the proposed method, with a particular focus on scenarios involving small local sample sizes, where quadratic approximations may fail to capture skewness and other higher-order characteristics of the log-likelihood function. They demonstrate that incorporating higher-order information of the log-likelihood function improves the accuracy while preserving the privacy, communication efficiency, and scalability required for collaborative biomedical and epidemiological research.
