# FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record

arXiv: https://arxiv.org/abs/1811.11400 · HTML: https://mlsanity.com/paper/1811.11400/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2018.

## Built on by (most cited first, 11 of 11)

- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions](https://mlsanity.com/paper/2208.03392/) (2022)
- [Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges](https://mlsanity.com/paper/2211.07893/) (2022)
- [Federated Generative Learning with Foundation Models](https://mlsanity.com/paper/2306.16064/) (2023)
- [Failure Prediction in Production Line Based on Federated Learning: An Empirical Study](https://mlsanity.com/paper/2101.11715/) (2021)
- [Towards the Practical Utility of Federated Learning in the Medical Domain](https://mlsanity.com/paper/2207.03075/) (2022)
- [Personalized Federated Deep Learning for Pain Estimation From Face Images](https://mlsanity.com/paper/2101.04800/) (2021)
- [On the Design of Communication-Efficient Federated Learning for Health Monitoring](https://mlsanity.com/paper/2211.16952/) (2022)
- [Federated Learning for Cyber Physical Systems: A Comprehensive Survey](https://mlsanity.com/paper/2505.04873/) (2025)
- [PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records](https://mlsanity.com/paper/2112.05321/) (2021)

## Abstract (arXiv)

Electronic health record (EHR) data is collected by individual institutions and often stored across locations in silos. Getting access to these data is difficult and slow due to security, privacy, regulatory, and operational issues. We show, using ICU data from 58 different hospitals, that machine learning models to predict patient mortality can be trained efficiently without moving health data out of their silos using a distributed machine learning strategy. We propose a new method, called Federated-Autonomous Deep Learning (FADL) that trains part of the model using all data sources in a distributed manner and other parts using data from specific data sources. We observed that FADL outperforms traditional federated learning strategy and conclude that balance between global and local training is an important factor to consider when design distributed machine learning methods , especially in healthcare.