# Privacy-Preserving Distributed Deep Learning for Clinical Data

arXiv: https://arxiv.org/abs/1812.01484 · HTML: https://mlsanity.com/paper/1812.01484/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2018.

## Built on by (most cited first, 8 of 8)

- [Does BERT Pretrained on Clinical Notes Reveal Sensitive Data?](https://mlsanity.com/paper/2104.07762/) (2021)
- [Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imaging](https://mlsanity.com/paper/2106.13208/) (2021)
- [Privacy-Preserving Customer Churn Prediction Model in the Context of Telecommunication Industry](https://mlsanity.com/paper/2411.01447/) (2024)
- [Ten Quick Tips for Deep Learning in Biology](https://mlsanity.com/paper/2105.14372/) (2021)
- [Federated Learning in Multi-Center Critical Care Research: A Systematic Case Study using the eICU Database](https://mlsanity.com/paper/2204.09328/) (2022)
- [Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends](https://mlsanity.com/paper/2501.04073/) (2025)
- [NanoBatch Privacy: Enabling fast Differentially Private learning on the IPU](https://mlsanity.com/paper/2109.12191/) (2021)
- [Distributed Deep Variational Approach for Privacy-preserving Data Release](https://mlsanity.com/paper/2605.03069/) (2026)

## Abstract (arXiv)

Deep learning with medical data often requires larger samples sizes than are available at single providers. While data sharing among institutions is desirable to train more accurate and sophisticated models, it can lead to severe privacy concerns due the sensitive nature of the data. This problem has motivated a number of studies on distributed training of neural networks that do not require direct sharing of the training data. However, simple distributed training does not offer provable privacy guarantees to satisfy technical safe standards and may reveal information about the underlying patients. We present a method to train neural networks for clinical data in a distributed fashion under differential privacy. We demonstrate these methods on two datasets that include information from multiple independent sites, the eICU collaborative Research Database and The Cancer Genome Atlas.