Privacy-Preserving Federated XGBoost with Learnable Learning Rates

Core Concepts

Developing a privacy-preserving framework for horizontal federated XGBoost without sharing gradients, enhancing communication efficiency.

Abstract

Introduction: Discusses the need for training XGBoost in federated learning due to privacy concerns. Existing Works: Highlights challenges in horizontal and vertical settings of federated XGBoost. Key Problems: Identifies per-node communication frequency and privacy concerns in traditional approaches. Proposed Solution: Introduces FedXGBllr, a novel framework with learnable learning rates for improved privacy and communication efficiency. Methodology: Describes the intuition behind using learnable learning rates and the aggregation process of tree ensembles. Experiments: Evaluates the performance of FedXGBllr on various datasets compared to existing methods. Results: Shows that FedXGBllr achieves comparable or better accuracy while reducing communication overhead significantly. Interpretability Analysis: Compares the interpretability and performance of different CNN architectures used in the framework.

Stats

The number of communication rounds can reach ∼100K. The total communication overhead is lower by factors ranging from 25x to 700x.

Quotes

"The adverse effects of data heterogeneity in FL over NN-based approaches are widely researched." "Using a fixed learning rate for each tree may be too weak since each tree can make different amounts of mistakes."

Key Insights Distilled From

Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates

by Chenyang Ma,... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2304.07537.pdf

Deeper Inquiries

How does FedXGBllr compare to other encryption-based methods in terms of communication overhead

FedXGBllr significantly reduces communication overhead compared to encryption-based methods. By not relying on the sharing of gradients and hessians, FedXGBllr eliminates the need for complex encryption protocols that can lead to high communication costs. The approach in FedXGBllr involves sending only the constructed tree ensembles to the server, resulting in a much lower communication overhead. This reduction is especially evident as dataset sizes scale up, with FedXGBllr saving communication costs by factors ranging from 25x to 700x compared to encryption-based methods.

What are the implications of reducing per-node level communication frequency on model performance

Reducing per-node level communication frequency has several implications on model performance: Efficiency: By disentangling per-node level communications, models trained using FedXGBllr become more efficient due to fewer rounds of communication required between clients and servers. Privacy: With reduced communication frequency, there are fewer opportunities for sensitive information like gradients and hessians to be leaked during transmission, enhancing privacy protection. Scalability: Models trained with lower per-node level communication frequencies can scale better across larger datasets or when dealing with a higher number of clients without compromising performance. Interpretability: Simplifying communications can also make it easier to interpret model decisions and understand how different components contribute to overall performance.

How might the concept of learnable learning rates be applied to other machine learning models beyond XGBoost

The concept of learnable learning rates introduced in FedXGBllr can be applied beyond XGBoost to other machine learning models as well: Neural Networks: Learnable learning rates could be incorporated into neural networks during training processes such as gradient descent optimization algorithms like Adam or SGD. Support Vector Machines (SVM): SVMs could benefit from adaptive learning rates based on data characteristics or model behavior during training iterations. Random Forests: Implementing learnable learning rates in Random Forest models could enhance their adaptability and improve predictive accuracy based on individual tree performances within the ensemble. K-means Clustering: Introducing learnable learning rates in K-means clustering algorithms could optimize cluster assignments iteratively based on data distribution shifts or cluster separations observed during training epochs. By incorporating learnable learning rates into various machine learning models, similar benefits seen in Federated XGBoost with improved adaptability and efficiency can be achieved across different domains and applications within machine learning frameworks.