Federated Learning for Clinical Structured Data: A Benchmark Comparison
  of Engineering and Statistical Approaches
Siqi Li Di Miao Qiming Wu Chuan Hong Danny D'Agostino Xin Li Yilin Ning Yuqing Shang Huazhu Fu Marcus Eng Hock Ong Hamed Haddadi Nan Liu
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.