# Generation of Synthetic Electronic Health Records Using a Federated GAN

arXiv: https://arxiv.org/abs/2109.02543 · HTML: https://mlsanity.com/paper/2109.02543/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 6 Sep 2021.

## Builds on (most cited first, 12 of 12)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [Generating Multi-label Discrete Patient Records using Generative Adversarial Networks](https://mlsanity.com/paper/1703.06490/) (2017)
- [Mode Regularized Generative Adversarial Networks](https://mlsanity.com/paper/1612.02136/) (2016)
- [BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning](https://mlsanity.com/paper/1905.06731/) (2019)
- [FedGAN: Federated Generative Adversarial Networks for Distributed Data](https://mlsanity.com/paper/2006.07228/) (2020)
- [CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare Records](https://mlsanity.com/paper/2001.09346/) (2020)
- [Locally Masked Convolution for Autoregressive Models](https://mlsanity.com/paper/2006.12486/) (2020)
- [Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach](https://mlsanity.com/paper/2102.12245/) (2021)

## Built on by (most cited first, 5 of 5)

- [ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitization](https://mlsanity.com/paper/2307.01946/) (2023)
- [A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources](https://mlsanity.com/paper/2203.07018/) (2022)
- [Modern Views of Machine Learning for Precision Psychiatry](https://mlsanity.com/paper/2204.01607/) (2022)
- [Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directions](https://mlsanity.com/paper/2311.17453/) (2023)
- [Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment](https://mlsanity.com/paper/2506.23358/) (2025)

## Abstract (arXiv)

Sensitive medical data is often subject to strict usage constraints. In this paper, we trained a generative adversarial network (GAN) on real-world electronic health records (EHR). It was then used to create a data-set of "fake" patients through synthetic data generation (SDG) to circumvent usage constraints. This real-world data was tabular, binary, intensive care unit (ICU) patient diagnosis data. The entire data-set was split into separate data silos to mimic real-world scenarios where multiple ICU units across different hospitals may have similarly structured data-sets within their own organisations but do not have access to each other's data-sets. We implemented federated learning (FL) to train separate GANs locally at each organisation, using their unique data silo and then combining the GANs into a single central GAN, without any siloed data ever being exposed. This global, central GAN was then used to generate the synthetic patients data-set. We performed an evaluation of these synthetic patients with statistical measures and through a structured review by a group of medical professionals. It was shown that there was no significant reduction in the quality of the synthetic EHR when we moved between training a single central model and training on separate data silos with individual models before combining them into a central model. This was true for both the statistical evaluation (Root Mean Square Error (RMSE) of 0.0154 for single-source vs. RMSE of 0.0169 for dual-source federated) and also for the medical professionals' evaluation (no quality difference between EHR generated from a single source and EHR generated from multiple sources).