# FedGA-Tree: Federated Decision Tree using Genetic Algorithm

arXiv: https://arxiv.org/abs/2506.08176 · HTML: https://mlsanity.com/paper/2506.08176/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 9 Jun 2025.

## Builds on (most cited first, 11 of 11)

- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [A Hybrid Approach to Privacy-Preserving Federated Learning](https://mlsanity.com/paper/1812.03224/) (2018)
- [SecureBoost: A Lossless Federated Learning Framework](https://mlsanity.com/paper/1901.08755/) (2019)
- [Practical Federated Gradient Boosting Decision Trees](https://mlsanity.com/paper/1911.04206/) (2019)
- [Federated Forest](https://mlsanity.com/paper/1905.10053/) (2019)
- [FederBoost: Private Federated Learning for GBDT](https://mlsanity.com/paper/2011.02796/) (2020)
- [An Efficient and Robust System for Vertically Federated Random Forest](https://mlsanity.com/paper/2201.10761/) (2022)
- [Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning](https://mlsanity.com/paper/2012.10602/) (2020)
- [Privacy-Preserving Collaborative Prediction using Random Forests](https://mlsanity.com/paper/1811.08695/) (2018)

## Abstract (arXiv)

In recent years, with rising concerns for data privacy, Federated Learning has gained prominence, as it enables collaborative training without the aggregation of raw data from participating clients. However, much of the current focus has been on parametric gradient-based models, while nonparametric counterparts such as decision tree are relatively understudied. Existing methods for adapting decision trees to Federated Learning generally combine a greedy tree-building algorithm with differential privacy to produce a global model for all clients. These methods are limited to classification trees and categorical data due to the constraints of differential privacy. In this paper, we explore an alternative approach that utilizes Genetic Algorithm to facilitate the construction of personalized decision trees and accommodate categorical and numerical data, thus allowing for both classification and regression trees. Comprehensive experiments demonstrate that our method surpasses decision trees trained solely on local data and a benchmark algorithm.