mlsanityData to 14 Oct 2026

An Efficient and Robust System for Vertically Federated Random Forest

arXiv 2201.10761 ↗First posted 26 Jan 2022cs.LG cs.CR cs.DCcited by 6

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Abstract (arXiv)

As there is a growing interest in utilizing data across multiple resources to build better machine learning models, many vertically federated learning algorithms have been proposed to preserve the data privacy of the participating organizations. However, the efficiency of existing vertically federated learning algorithms remains to be a big problem, especially when applied to large-scale real-world datasets. In this paper, we present a fast, accurate, scalable and yet robust system for vertically federated random forest. With extensive optimization, we achieved 5× and 83× speed up over the SOTA SecureBoost model citecheng2019secureboost for training and serving tasks. Moreover, the proposed system can achieve similar accuracy but with favorable scalability and partition tolerance. Our code has been made public to facilitate the development of the community and the protection of user data privacy.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 17 of its 37 references that are in the map. First posted on arXiv 26 Jan 2022.

Builds on

17 of 37 references.

References · 17

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  5. 2019Towards Federated Learning at Scale: System Designcited by 832
  6. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  7. 2019Federated Machine Learning: Concept and Applicationscited by 412
  8. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  9. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 176
  10. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 176
  11. 2021Federated Learning for Open Bankingcited by 141
  12. 2019Federated Forestcited by 49
5 more
  1. 2020Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Datacited by 36
  2. 2019Stochastic Proximal AUC Maximizationcited by 14
  3. 2020ByzShield: An Efficient and Robust System for Distributed Trainingcited by 8
  4. 2020Industrial Federated Learning -- Requirements and System Designcited by 6
  5. 2021Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platformcited by 4

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 2.

6 papers.

  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 108
  2. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  3. 2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learningcited by 8
  4. 2023Eliminating Label Leakage in Tree-Based Vertical Federated Learningcited by 6
  5. 2025Tree-based Models for Vertical Federated Learning: A Surveycited by 3
  6. 2025FedGA-Tree: Federated Decision Tree using Genetic Algorithmcited by 0