FedGA-Tree: Federated Decision Tree using Genetic Algorithm

TL;DR

This paper introduces FedGA-Tree, a federated decision tree method using genetic algorithms that supports both classification and regression for categorical and numerical data, enhancing privacy-preserving collaborative learning.

Contribution

It presents a novel genetic algorithm-based approach for federated decision trees, enabling personalized models and handling diverse data types beyond existing differential privacy methods.

Findings

Outperforms local-only decision trees in accuracy

Surpasses benchmark federated decision tree algorithms

Supports both classification and regression tasks

Abstract

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.