Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment
Journal:
arXiv
Published Date:
Jun 29, 2025
Abstract
We present Federated Timeline Synthesis (FTS), a novel framework for training
generative foundation models across distributed timeseries data applied to
electronic health records (EHR). At its core, FTS represents patient history as
tokenized Patient Health Timelines (PHTs), language-agnostic sequences encoding
temporal, categorical, and continuous clinical information. Each institution
trains an autoregressive transformer on its local PHTs and transmits only model
weights to a central server. The server uses the generators to synthesize a
large corpus of trajectories and train a Global Generator (GG), enabling
zero-shot inference via Monte Carlo simulation of future PHTs. We evaluate FTS
on five clinically meaningful prediction tasks using MIMIC-IV data, showing
that models trained on synthetic data generated by GG perform comparably to
those trained on real data. FTS offers strong privacy guarantees, scalability
across institutions, and extensibility to diverse prediction and simulation
tasks especially in healthcare, including counterfactual inference, early
warning detection, and synthetic trial design.
