  Federated learning (FL) has gained popularity in clinical research in recent
years to facilitate privacy-preserving collaboration. Structured data, one of
the most prevalent forms of clinical data, has experienced significant growth
in volume concurrently, notably with the widespread adoption of electronic
health records in clinical practice. This review examines FL applications on
structured medical data, identifies contemporary limitations and discusses
potential innovations. We searched five databases, SCOPUS, MEDLINE, Web of
Science, Embase, and CINAHL, to identify articles that applied FL to structured
medical data and reported results following the PRISMA guidelines. Each
selected publication was evaluated from three primary perspectives, including
data quality, modeling strategies, and FL frameworks. Out of the 1160 papers
screened, 34 met the inclusion criteria, with each article consisting of one or
more studies that used FL to handle structured clinical/medical data. Of these,
24 utilized data acquired from electronic health records, with clinical
predictions and association studies being the most common clinical research
tasks that FL was applied to. Only one article exclusively explored the
vertical FL setting, while the remaining 33 explored the horizontal FL setting,
with only 14 discussing comparisons between single-site (local) and FL (global)
analysis. The existing FL applications on structured medical data lack
sufficient evaluations of clinically meaningful benefits, particularly when
compared to single-site analyses. Therefore, it is crucial for future FL
applications to prioritize clinical motivations and develop designs and
methodologies that can effectively support and aid clinical practice and
research.
