Histogram-Based Federated XGBoost with Minimal Variance Sampling for Improved Performance on Federated Tabular Data

Core Concepts

Federated XGBoost using Minimal Variance Sampling (MVS) can improve performance in terms of accuracy and regression error compared to federated XGBoost with no sampling or uniform sampling on federated tabular datasets.

Abstract

The paper proposes a federated XGBoost model that uses Minimal Variance Sampling (MVS) to select training data when building decision trees. The authors evaluate this model, called F-XGB, on a set of federated tabular datasets and compare its performance to federated XGBoost with no sampling (NS) and uniform sampling (U).
The key findings are:

F-XGB using MVS outperforms F-XGB with NS and U in almost all cases, achieving better accuracy, F1 scores, AUC, and lower regression error.
F-XGB using MVS with a 50% sampling fraction performs best on larger and multiclass datasets, while 10-20% sampling fraction works better for smaller and binary classification datasets.
F-XGB using MVS outperforms centralized XGBoost in half of the studied cases.
F-XGB using MVS can improve local performance on client datasets compared to global performance, indicating it can better optimize for local data distributions.
The authors introduce "FedTab", a collection of federated tabular datasets for benchmarking federated learning methods.
Overall, the results demonstrate that incorporating sampling techniques like MVS can significantly enhance the performance of federated XGBoost on tabular data, outperforming both federated XGBoost without sampling and centralized XGBoost in many cases.

Stats

Federated XGBoost using MVS with 50% sampling fraction achieves 93.5% accuracy on FEMNIST dataset, compared to 89.9% for uniform sampling and 89.7% for no sampling.
On the Insurance Premium Prediction regression task, Federated XGBoost using MVS with 20% sampling fraction achieves an RMSE of 4082, compared to 4310 for uniform sampling and 4496 for no sampling.
Federated XGBoost using MVS outperforms centralized XGBoost on 3 out of the 6 datasets studied.

Quotes

"Federated XGBoost using MVS improves performance in terms of accuracy and regression error when compared with federated XGBoost using no- or uniform sampling."
"Federated XGBoost using MVS performs similarly as centralized, and even outperforms it in half of the cases."

Key Insights Distilled From

Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data

by William Lind... at arxiv.org 05-06-2024

https://arxiv.org/pdf/2405.02067.pdf

Deeper Inquiries

How would the performance of Federated XGBoost using MVS compare to other advanced federated tree-based models, such as Federated Random Forests or Federated LightGBM

In comparing the performance of Federated XGBoost using Minimal Variance Sampling (MVS) to other advanced federated tree-based models like Federated Random Forests or Federated LightGBM, several factors come into play.
Federated XGBoost with MVS has shown promising results in the context provided. It has demonstrated improved accuracy and regression error in a federated setting, outperforming centralized XGBoost in some cases. When compared to other advanced federated tree-based models, the performance of Federated XGBoost using MVS would likely depend on the specific characteristics of the dataset and the nature of the task at hand.
Federated Random Forests, for example, have been explored in the literature as a decentralized approach to building random forests in a federated learning setting. While Federated Random Forests have shown potential, they may face challenges in terms of scalability and efficiency due to the complexity of ensemble methods in a distributed environment. On the other hand, Federated LightGBM, a distributed gradient boosting framework, offers high efficiency and scalability, making it a strong competitor to Federated XGBoost using MVS.
In a direct comparison, Federated XGBoost using MVS may excel in scenarios where the dataset exhibits non-IID properties, as MVS aims to minimize variance in sample selection. This can lead to more stable and informative training examples, potentially improving model performance. However, the performance comparison would ultimately depend on the specific characteristics of the dataset, the complexity of the task, and the efficiency of the federated learning framework in handling distributed data and computations.

What are the potential limitations or drawbacks of using MVS in a federated setting compared to a centralized setting, and how could these be addressed

Using Minimal Variance Sampling (MVS) in a federated setting introduces certain limitations and drawbacks compared to a centralized setting. These limitations include:

Communication Overhead: Implementing MVS in a federated setting may increase communication overhead between the clients and the central aggregator. Since MVS involves selecting samples based on gradients and hessians, the exchange of this information can lead to increased communication costs.

Privacy Concerns: MVS requires sharing gradient and hessian information for sample selection, which could potentially compromise the privacy of individual client data. Ensuring data privacy and security becomes crucial when using MVS in a federated learning environment.

Computational Complexity: Calculating gradients and hessians for sample selection in MVS can introduce computational complexity, especially in a distributed setting where resources are limited. This complexity may impact the scalability and efficiency of the federated learning process.

To address these limitations, several strategies can be considered:

Privacy-Preserving Techniques: Implementing privacy-preserving techniques like differential privacy or secure multi-party computation can help protect sensitive information during the sample selection process.

Optimization Algorithms: Developing optimized algorithms for calculating gradients and hessians in a distributed environment can reduce computational overhead and improve efficiency.

Communication Optimization: Implementing efficient communication protocols, such as federated averaging or secure aggregation, can help minimize communication costs while ensuring effective sample selection.

By addressing these limitations and drawbacks, the use of MVS in a federated setting can be optimized for improved performance and privacy protection.

Could the insights from this work on sampling techniques be extended to other federated machine learning models beyond tree-based methods, such as neural networks or deep learning models

The insights gained from this work on sampling techniques, particularly Minimal Variance Sampling (MVS), can be extended to other federated machine learning models beyond tree-based methods, such as neural networks or deep learning models.

Gradient-Based Sampling: Similar to how MVS uses gradients and hessians for sample selection in tree-based models, gradient-based sampling techniques can be applied to neural networks in a federated setting. By selecting samples based on gradients, models can focus on informative data points for training, potentially improving performance.

Variance Reduction Techniques: Techniques that aim to minimize variance in sample selection, like MVS, can be adapted for neural networks. By selecting samples with low variance in predictions, neural network models can train on more stable and representative data, leading to better generalization and performance.

Privacy-Preserving Sampling: Integrating privacy-preserving sampling methods with neural networks in federated learning can enhance data security while ensuring effective sample selection. Techniques like federated averaging and secure aggregation can be utilized to protect sensitive information during the sampling process.

By extending the insights from sampling techniques to neural networks and deep learning models in a federated setting, researchers can explore new avenues for improving the efficiency, performance, and privacy of federated machine learning across a broader range of model architectures.