mlsanityData to 7 Oct 2026

Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates

arXiv 2304.07537 ↗First posted 15 Apr 2023cs.LG cs.AI cs.DCcited by 12

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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/

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 23 of its 39 references that are in the map. First posted on arXiv 15 Apr 2023.

Builds on

23 of 39 references.

References · 23

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  4. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  5. 2019Advances and Open Problems in Federated Learningcited by 3,021
  6. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  7. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  8. 2020Adaptive Federated Optimizationcited by 1,036
  9. 2019Deep Leakage from Gradientscited by 1,004
  10. 2019Towards Federated Learning at Scale: System Designcited by 826
  11. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  12. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
11 more
  1. 2021Tabular Data: Deep Learning is Not All You Needcited by 530
  2. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  3. 2022Why do tree-based models still outperform deep learning on tabular data?cited by 198
  4. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 174
  5. 2020A Semi-supervised Graph Attentive Network for Financial Fraud Detectioncited by 150
  6. 2019Practical Federated Gradient Boosting Decision Treescited by 53
  7. 2020Survival regression with accelerated failure time model in XGBoostcited by 37
  8. 2020FederBoost: Private Federated Learning for GBDTcited by 27
  9. 2019The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoostcited by 13
  10. 2020Adaptive Histogram-Based Gradient Boosted Trees for Federated Learningcited by 10
  11. 2022Federated XGBoost on Sample-Wise Non-IID Datacited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 4; 2025: 1; 2026: 5.

12 papers.

  1. 2024SpatialPIN: Enhancing Spatial Reasoning Capabilities of Vision-Language Models through Prompting and Interacting 3D Priorscited by 53
  2. 2023Boosting Detection in Crowd Analysis via Underutilized Output Featurescited by 14
  3. 2026CycleVLA: Proactive Self-Correcting Vision-Language-Action Models via Subtask Backtracking and Minimum Bayes Risk Decodingcited by 13
  4. 2023VCISR: Blind Single Image Super-Resolution with Video Compression Synthetic Datacited by 7
  5. 2024The Effects of Data Imbalance Under a Federated Learning Approach for Credit Risk Forecastingcited by 2
  6. 2024A collaborative ensemble construction method for federated random forestcited by 1
  7. 2026On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learningcited by 1
  8. 2024ADVENT: Attack/Anomaly Detection in VANETscited by 0
  9. 2025TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systemscited by 0
  10. 2026Bikelution: Federated Gradient-Boosting for Scalable Shared Micro-Mobility Demand Forecastingcited by 0
  11. 2026FedSCS-XGB -- Federated Server-centric surrogate XGBoost for continual health monitoringcited by 0
  12. 2026Federated Hyperdimensional Computing for Resource-Constrained Industrial IoTcited by 0