# Federated machine learning with Anonymous Random Hybridization (FeARH) on medical records

arXiv: https://arxiv.org/abs/2001.09751 · HTML: https://mlsanity.com/paper/2001.09751/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [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)
- [Federated and distributed learning applications for electronic health records and structured medical data: A scoping review](https://mlsanity.com/paper/2304.07310/) (2023)

## Abstract (arXiv)

Sometimes electrical medical records are restricted and difficult to centralize for machine learning, which could only be trained in distributed manner that involved many institutions in the process. However, sometimes some institutions are likely to figure out the private data used for training certain models based on the parameters they obtained, which is a violation of privacy and certain regulations. Under those circumstances, we develop an algorithm, called 'federated machine learning with anonymous random hybridization'(abbreviated as 'FeARH'), using mainly hybridization algorithm to eliminate connections between medical record data and models' parameters, which avoid untrustworthy institutions from stealing patients' private medical records. Based on our experiment, our new algorithm has similar AUCROC and AUCPR result compared with machine learning in centralized manner and original federated machine learning, at the same time, our algorithm can greatly reduce data transfer size in comparison with original federated machine learning.