Federated learning (FL) has shown promising potential in safeguarding data privacy in healthcare collaborations. While the term "FL" was originally coined by the engineering community, the statistical field has also explored similar privacy-preserving algorithms. Statistical FL algorithms, however, remain considerably less recognized than their engineering counterparts. Our goal was to bridge the gap by presenting the first comprehensive comparison of FL frameworks from both engineering and statistical domains. We evaluated five FL frameworks using both simulated and real-world data. The results indicate that statistical FL algorithms yield less biased point estimates for model coefficients and offer convenient confidence interval estimations. In contrast, engineering-based methods tend to generate more accurate predictions, sometimes surpassing central pooled and statistical FL models. This study underscores the relative strengths and weaknesses of both types of methods, emphasizing the need for increased awareness and their integration in future FL applications.

通过评估五个FL框架的模拟和真实数据，本研究首次全面比较了工程和统计领域的FL框架，结果表明，相对于工程方法，统计FL算法在模型系数的估计上产生的偏差更小，且提供了方便的置信区间估计。然而，工程方法往往能够生成更准确的预测，有时甚至超过了中心汇总和统计FL模型。这项研究强调了两种方法的相对优势和劣势，并强调了未来FL应用中增加对两种方法的认识和整合的重要性。

临床结构化数据的联邦学习：工程和统计方法的基准比较