# Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning

arXiv: https://arxiv.org/abs/2012.06670 · HTML: https://mlsanity.com/paper/2012.06670/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2020.

## Built on by (most cited first, 10 of 10)

- [Privacy-Preserving Machine Learning: Methods, Challenges and Directions](https://mlsanity.com/paper/2108.04417/) (2021)
- [FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning](https://mlsanity.com/paper/2112.08524/) (2021)
- [Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates](https://mlsanity.com/paper/2304.07537/) (2023)
- [Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning](https://mlsanity.com/paper/2105.09540/) (2021)
- [Federated XGBoost on Sample-Wise Non-IID Data](https://mlsanity.com/paper/2209.01340/) (2022)
- [Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis](https://mlsanity.com/paper/2202.08338/) (2022)
- [Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach](https://mlsanity.com/paper/2202.12443/) (2022)
- [Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data](https://mlsanity.com/paper/2405.02067/) (2024)
- [A Case for Library-Level k-Means Binning in Histogram Gradient-Boosted Trees](https://mlsanity.com/paper/2505.12460/) (2025)
- [FedSCS-XGB -- Federated Server-centric surrogate XGBoost for continual health monitoring](https://mlsanity.com/paper/2603.06224/) (2026)

## Abstract (arXiv)

Federated Learning (FL) is an approach to collaboratively train a model across multiple parties without sharing data between parties or an aggregator. It is used both in the consumer domain to protect personal data as well as in enterprise settings, where dealing with data domicile regulation and the pragmatics of data silos are the main drivers. While gradient boosted tree implementations such as XGBoost have been very successful for many use cases, its federated learning adaptations tend to be very slow due to using cryptographic and privacy methods and have not experienced widespread use. We propose the Party-Adaptive XGBoost (PAX) for federated learning, a novel implementation of gradient boosting which utilizes a party adaptive histogram aggregation method, without the need for data encryption. It constructs a surrogate representation of the data distribution for finding splits of the decision tree. Our experimental results demonstrate strong model performance, especially on non-IID distributions, and significantly faster training run-time across different data sets than existing federated implementations. This approach makes the use of gradient boosted trees practical in enterprise federated learning.