用遗传算法实现联邦决策树，支持分类回归与数值类别数据。
FedGA-Tree: Federated Decision Tree using Genetic Algorithm
- 用遗传算法优化全局决策树结构，避免依赖梯度和差分隐私。
- 在分类与回归任务上均优于本地训练模型和基准方法。
- 适合需要个性化建模且数据含混合类型的应用场景。
近年来，随着数据隐私问题日益受到关注，联邦学习因其能够在不聚合原始数据的情况下实现协作训练而备受重视。然而，当前研究多集中于基于梯度的参数化模型，而非参数化模型如决策树则相对较少被研究。现有将决策树应用于联邦学习的方法通常结合贪婪树构建算法与差分隐私，以生成对所有客户端通用的全局模型，但受限于差分隐私，这些方法仅适用于分类任务和分类数据。本文提出一种新方法：利用遗传算法构建个性化决策树，支持分类与回归任务，并能处理数值型与类别型数据。大量实验表明，该方法在性能上超越仅在本地数据上训练的决策树及基准算法。
原文摘要 · Abstract (English)
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.
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