# Federated Rule Ensemble Method in Medical Data

arXiv: https://arxiv.org/abs/2604.17956 · HTML: https://mlsanity.com/paper/2604.17956/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 20 Apr 2026.

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

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation](https://mlsanity.com/paper/2305.02231/) (2023)
- [Federated Composite Optimization](https://mlsanity.com/paper/2011.08474/) (2020)
- [A Survey on Heterogeneous Federated Learning](https://mlsanity.com/paper/2210.04505/) (2022)
- [Interpret Federated Learning with Shapley Values](https://mlsanity.com/paper/1905.04519/) (2019)
- [Towards Interpretable Federated Learning](https://mlsanity.com/paper/2302.13473/) (2023)
- [Going Digital: A Survey on Digitalization and Large Scale Data Analytics in Healthcare](https://mlsanity.com/paper/1606.08075/) (2016)
- [Bayesian Federated Inference for estimating Statistical Models based on Non-shared Multicenter Data sets](https://mlsanity.com/paper/2302.07677/) (2023)
- [An Interpretable Client Decision Tree Aggregation process for Federated Learning](https://mlsanity.com/paper/2404.02510/) (2024)
- [FLEX: FLEXible Federated Learning Framework](https://mlsanity.com/paper/2404.06127/) (2024)

## Abstract (arXiv)

Machine learning has become integral to medical research and is increasingly applied in clinical settings to support diagnosis and decision-making; however, its effectiveness depends on access to large, diverse datasets, which are limited within single institutions. Although integrating data across institutions can address this limitation, privacy regulations and data ownership constraints hinder these efforts. Federated learning enables collaborative model training without sharing raw data; however, most methods rely on complex architectures that lack interpretability, limiting clinical applicability. Therefore, we proposed a federated RuleFit framework to construct a unified and interpretable global model for distributed environments. It integrates three components: preprocessing based on differentially private histograms to estimate shared cutoff values, enabling consistent rule definitions and reducing heterogeneity across clients; local rule generation using gradient boosting decision trees with shared cutoffs; and coefficient estimation via ℓ1-regularized optimization using a Federated Dual Averaging algorithm for sparse and consistent variable selection. In simulation studies, the proposed method achieved a performance comparable to that of centralized RuleFit while outperforming existing federated approaches. Real-world analysis demonstrated its ability to provide interpretable insights with competitive predictive accuracy. Therefore, the proposed framework offers a practical and effective solution for interpretable and reliable modeling in federated learning environments.