# Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates

arXiv: https://arxiv.org/abs/2304.07537 · HTML: https://mlsanity.com/paper/2304.07537/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 39 references that are in the map.
- First posted on arXiv 15 Apr 2023.

## Builds on (most cited first, 23 of 23)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [FedBN: Federated Learning on Non-IID Features via Local Batch Normalization](https://mlsanity.com/paper/2102.07623/) (2021)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Why do tree-based models still outperform deep learning on tabular data?](https://mlsanity.com/paper/2207.08815/) (2022)
- [SecureBoost: A Lossless Federated Learning Framework](https://mlsanity.com/paper/1901.08755/) (2019)
- [A Semi-supervised Graph Attentive Network for Financial Fraud Detection](https://mlsanity.com/paper/2003.01171/) (2020)
- [Practical Federated Gradient Boosting Decision Trees](https://mlsanity.com/paper/1911.04206/) (2019)
- [Survival regression with accelerated failure time model in XGBoost](https://mlsanity.com/paper/2006.04920/) (2020)
- [FederBoost: Private Federated Learning for GBDT](https://mlsanity.com/paper/2011.02796/) (2020)
- [The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost](https://mlsanity.com/paper/1907.07157/) (2019)
- [Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning](https://mlsanity.com/paper/2012.06670/) (2020)
- [Federated XGBoost on Sample-Wise Non-IID Data](https://mlsanity.com/paper/2209.01340/) (2022)

## Built on by (most cited first, 12 of 12)

- [SpatialPIN: Enhancing Spatial Reasoning Capabilities of Vision-Language Models through Prompting and Interacting 3D Priors](https://mlsanity.com/paper/2403.13438/) (2024)
- [Boosting Detection in Crowd Analysis via Underutilized Output Features](https://mlsanity.com/paper/2308.16187/) (2023)
- [CycleVLA: Proactive Self-Correcting Vision-Language-Action Models via Subtask Backtracking and Minimum Bayes Risk Decoding](https://mlsanity.com/paper/2601.02295/) (2026)
- [VCISR: Blind Single Image Super-Resolution with Video Compression Synthetic Data](https://mlsanity.com/paper/2311.00996/) (2023)
- [The Effects of Data Imbalance Under a Federated Learning Approach for Credit Risk Forecasting](https://mlsanity.com/paper/2401.07234/) (2024)
- [A collaborative ensemble construction method for federated random forest](https://mlsanity.com/paper/2407.19193/) (2024)
- [On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learning](https://mlsanity.com/paper/2602.20782/) (2026)
- [ADVENT: Attack/Anomaly Detection in VANETs](https://mlsanity.com/paper/2401.08564/) (2024)
- [TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems](https://mlsanity.com/paper/2506.07605/) (2025)
- [Bikelution: Federated Gradient-Boosting for Scalable Shared Micro-Mobility Demand Forecasting](https://mlsanity.com/paper/2602.20671/) (2026)
- [FedSCS-XGB -- Federated Server-centric surrogate XGBoost for continual health monitoring](https://mlsanity.com/paper/2603.06224/) (2026)
- [Federated Hyperdimensional Computing for Resource-Constrained Industrial IoT](https://mlsanity.com/paper/2603.20037/) (2026)

## Abstract (arXiv)

The privacy-sensitive nature of decentralized datasets and the robustness of eXtreme Gradient Boosting (XGBoost) on tabular data raise the needs to train XGBoost in the context of federated learning (FL). Existing works on federated XGBoost in the horizontal setting rely on the sharing of gradients, which induce per-node level communication frequency and serious privacy concerns. To alleviate these problems, we develop an innovative framework for horizontal federated XGBoost which does not depend on the sharing of gradients and simultaneously boosts privacy and communication efficiency by making the learning rates of the aggregated tree ensembles learnable. We conduct extensive evaluations on various classification and regression datasets, showing our approach achieves performance comparable to the state-of-the-art method and effectively improves communication efficiency by lowering both communication rounds and communication overhead by factors ranging from 25x to 700x. Project Page: https://flower.ai/blog/2023-04-19-xgboost-with-flower/