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

Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches