mlsanityData to 7 Oct 2026

A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression

arXiv 1912.00513 ↗First posted 1 Dec 2019cs.LG stat.MLcited by 37

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Abstract (arXiv)

Data privacy and security becomes a major concern in building machine learning models from different data providers. Federated learning shows promise by leaving data at providers locally and exchanging encrypted information. This paper studies the vertical federated learning structure for logistic regression where the data sets at two parties have the same sample IDs but own disjoint subsets of features. Existing frameworks adopt the first-order stochastic gradient descent algorithm, which requires large number of communication rounds. To address the communication challenge, we propose a quasi-Newton method based vertical federated learning framework for logistic regression under the additively homomorphic encryption scheme. Our approach can considerably reduce the number of communication rounds with a little additional communication cost per round. Numerical results demonstrate the advantages of our approach over the first-order method.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Dec 2019.

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Citing papers per year: 2021: 11; 2022: 10; 2023: 10; 2024: 3; 2025: 3.

37 papers.

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  1. 2023Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignmentcited by 394
  2. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  3. 2021Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updatingcited by 37
  4. 2021FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Datacited by 31
  5. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  6. 2022Differentially Private Label Protection in Split Learningcited by 25
  7. 2022FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learningcited by 23
  8. 2022SphereFed: Hyperspherical Federated Learningcited by 19
  9. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  10. 2021AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilizationcited by 16
  11. 2021ExPLoit: Extracting Private Labels in Split Learningcited by 15
  12. 2022Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clusteringcited by 14
25 more
  1. 2021Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learningcited by 10
  2. 2021Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategiescited by 9
  3. 2022Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithmcited by 9
  4. 2023Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Partiescited by 7
  5. 2023Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimizationcited by 7
  6. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  7. 2021Efficient Batch Homomorphic Encryption for Vertically Federated XGBoostcited by 6
  8. 2022FedGBF: An efficient vertical federated learning framework via gradient boosting and baggingcited by 5
  9. 2021A Vertical Federated Learning Framework for Horizontally Partitioned Labelscited by 4
  10. 2022Differentially Private Vertical Federated Learningcited by 4
  11. 2023Secure Vertical Federated Learning Under Unreliable Connectivitycited by 4
  12. 2023GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Datacited by 3
  13. 2024URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learningcited by 3
  14. 2025Federated Granger Causality Learning for Interdependent Clients with State Space Representationcited by 3
  15. 2021Vertical federated learning based on DFP and BFGScited by 2
  16. 2021SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Treecited by 2
  17. 2022Privacy Against Inference Attacks in Vertical Federated Learningcited by 2
  18. 2023Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimizationcited by 2
  19. 2023Privacy Against Agnostic Inference Attacks in Vertical Federated Learningcited by 1
  20. 2023Quadratic Functional Encryption for Secure Training in Vertical Federated Learningcited by 1
  21. 2023Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddingscited by 1
  22. 2024PackVFL: Efficient HE Packing for Vertical Federated Learningcited by 1
  23. 2022Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restrictioncited by 0
  24. 2025FedOSAA: Improving Federated Learning with One-Step Anderson Accelerationcited by 0
  25. 2025Event-Driven Online Vertical Federated Learningcited by 0