  Federated or multi-site studies have distinct advantages over single-site
studies, including increased generalizability, the ability to study
underrepresented populations, and the opportunity to study rare exposures and
outcomes. However, these studies are challenging due to the need to preserve
the privacy of each individual's data and the heterogeneity in their covariate
distributions. We propose a novel federated approach to derive valid causal
inferences for a target population using multi-site data. We adjust for
covariate shift and covariate mismatch between sites by developing
multiply-robust and privacy-preserving nuisance function estimation. Our
methodology incorporates transfer learning to estimate ensemble weights to
combine information from source sites. We show that these learned weights are
efficient and optimal under different scenarios. We showcase the finite sample
advantages of our approach in terms of efficiency and robustness compared to
existing approaches.
