Federated Timeline Synthesis: An Empirical Study of Generative Knowledge Transfer

Synthetic patient timelines offer a means of transferring learned information between electronic health record models without pooling raw longitudinal records. We study Federated Timeline Synthesis (FTS), a one-shot protocol in which clients share locally trained autoregressive generators and a server trains a global generator on their synthetic timelines. Building on ETHOS, we evaluate eleven training configurations across nine structured EHR prediction endpoints using three disjoint, harmonized subsets of MIMIC-IV. The experiments distinguish synthetic-only transfer, multi-source pooling, and augmentation of smaller real-data sources. In the smallest-source setting, augmentation with timelines from a larger-source generator increases DRG accuracy from 0.364 to 0.717 and hospital mortality AUROC from 0.835 to 0.893, with higher point estimates on all nine endpoints. In the larger recipient setting, gains are more task dependent, while adding synthetic sources does not consistently improve performance. These results support synthetic timelines as a mechanism for transferring predictive information between generative EHR models, while showing that the benefit depends strongly on the recipient and training configuration. We publish code: https://anonymous.4open.science/r/fts.