FedEHR-Gen: Federated Synthetic Time-Series EHR Generation via Latent Space Alignment and Distribution-Aware Aggregation
arXiv:2605.27892v1 Announce Type: new Abstract: Synthetic Electronic Health Record (EHR) generation provides a promising avenue for data augmentation and cross-hospital modeling in privacy-constrained healthcare settings. However, most existing EHR generative models are centralized and require pooling data across hospitals, which is often infeasible when real-world data sharing is restricted. While federated EHR generation offers a natural solution, direct federated modeling often collapses or diverges due to the high dimensionality, sparsity, and cross-hospital heterogeneity of EHR data. In t
The increasing emphasis on data privacy regulations and the technical challenges of sharing sensitive healthcare data are driving demand for privacy-preserving AI methods.
This development allows for the leveraging of vast, distributed healthcare datasets for AI model training without compromising patient privacy, accelerating medical research and personalized medicine.
The ability to generate synthetic EHRs in a federated manner mitigates the need for centralized data pooling, enabling cross-institutional AI development even where data sharing is restricted.
- · Healthcare AI developers
- · Hospitals and research institutions
- · Patients (through improved diagnostics/treatments)
- · Data intermediaries relying on direct EHR access
- · Centralized data warehouse providers
More robust and generalizable AI models for healthcare will be developed faster across distributed hospital networks.
Accelerated discovery of new treatment protocols or diagnostic markers due to enhanced AI model performance on diverse patient populations.
Federated synthetic data generation could become a standard for R&D in other privacy-sensitive sectors beyond healthcare, fostering broader AI adoption where data silos currently exist.
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Read at arXiv cs.LG