# Federated and Differentially Private Learning for Electronic Health Records

arXiv: https://arxiv.org/abs/1911.05861 · HTML: https://mlsanity.com/paper/1911.05861/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2019.

## Built on by (most cited first, 14 of 14)

- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [Algorithm Fairness in AI for Medicine and Healthcare](https://mlsanity.com/paper/2110.00603/) (2021)
- [Dopamine: Differentially Private Federated Learning on Medical Data](https://mlsanity.com/paper/2101.11693/) (2021)
- [Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods](https://mlsanity.com/paper/2305.11386/) (2023)
- [Failure Prediction in Production Line Based on Federated Learning: An Empirical Study](https://mlsanity.com/paper/2101.11715/) (2021)
- [A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things](https://mlsanity.com/paper/2104.10501/) (2021)
- [Federated learning: Applications, challenges and future directions](https://mlsanity.com/paper/2205.09513/) (2022)
- [Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling](https://mlsanity.com/paper/2204.14017/) (2022)
- [Federated Learning for Cyber Physical Systems: A Comprehensive Survey](https://mlsanity.com/paper/2505.04873/) (2025)
- [Exploring Lightweight Federated Learning for Distributed Load Forecasting](https://mlsanity.com/paper/2404.03320/) (2024)
- [Federated Learning in Multi-Center Critical Care Research: A Systematic Case Study using the eICU Database](https://mlsanity.com/paper/2204.09328/) (2022)
- [FedCVD++: Communication-Efficient Federated Learning for Cardiovascular Risk Prediction with Parametric and Non-Parametric Model Optimization](https://mlsanity.com/paper/2507.22963/) (2025)
- [FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data](https://mlsanity.com/paper/2605.18936/) (2026)
- [Protocol Choices Matter More Than the Aggregation Algorithm: Federated Learning for In-Hospital Mortality Prediction in Care-Unit and Multi-Hospital Federations](https://mlsanity.com/paper/2610.17265/) (2026)

## Abstract (arXiv)

The use of collaborative and decentralized machine learning techniques such as federated learning have the potential to enable the development and deployment of clinical risk predictions models in low-resource settings without requiring sensitive data be shared or stored in a central repository. This process necessitates communication of model weights or updates between collaborating entities, but it is unclear to what extent patient privacy is compromised as a result. To gain insight into this question, we study the efficacy of centralized versus federated learning in both private and non-private settings. The clinical prediction tasks we consider are the prediction of prolonged length of stay and in-hospital mortality across thirty one hospitals in the eICU Collaborative Research Database. We find that while it is straightforward to apply differentially private stochastic gradient descent to achieve strong privacy bounds when training in a centralized setting, it is considerably more difficult to do so in the federated setting.