A Federated Random Forest Solution for Secure Distributed Machine Learning

TL;DR

This paper presents a federated learning framework for Random Forest classifiers that ensures data privacy, supports distributed training, and achieves competitive accuracy in healthcare applications, addressing a gap in privacy-preserving, interpretable machine learning.

Contribution

The paper introduces a novel federated Random Forest framework using PySyft, enabling privacy-preserving, distributed training with features like weighted averaging and incremental learning.

Findings

Maintains within 9% accuracy of centralized models

Supports privacy-preserving distributed training

Demonstrates effectiveness on healthcare benchmarks

Abstract

Privacy and regulatory barriers often hinder centralized machine learning solutions, particularly in sectors like healthcare where data cannot be freely shared. Federated learning has emerged as a powerful paradigm to address these concerns; however, existing frameworks primarily support gradient-based models, leaving a gap for more interpretable, tree-based approaches. This paper introduces a federated learning framework for Random Forest classifiers that preserves data privacy and provides robust performance in distributed settings. By leveraging PySyft for secure, privacy-aware computation, our method enables multiple institutions to collaboratively train Random Forest models on locally stored data without exposing sensitive information. The framework supports weighted model averaging to account for varying data distributions, incremental learning to progressively refine models, and local evaluation to assess performance across heterogeneous datasets. Experiments on two real-world healthcare benchmarks demonstrate that the federated approach maintains competitive predictive accuracy - within a maximum 9\% margin of centralized methods - while satisfying stringent privacy requirements. These findings underscore the viability of tree-based federated learning for scenarios where data cannot be centralized due to regulatory, competitive, or technical constraints. The proposed solution addresses a notable gap in existing federated learning libraries, offering an adaptable tool for secure distributed machine learning tasks that demand both transparency and reliable performance. The tool is available at https://github.com/ieeta-pt/fed_rf.