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

arXiv: https://arxiv.org/abs/1912.00513 · HTML: https://mlsanity.com/paper/1912.00513/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2019.

## Built on by (most cited first, 30 of 38)

- [Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment](https://mlsanity.com/paper/2308.05374/) (2023)
- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating](https://mlsanity.com/paper/2103.00958/) (2021)
- [FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data](https://mlsanity.com/paper/2103.03918/) (2021)
- [A Survey on Vertical Federated Learning: From a Layered Perspective](https://mlsanity.com/paper/2304.01829/) (2023)
- [Differentially Private Label Protection in Split Learning](https://mlsanity.com/paper/2203.02073/) (2022)
- [FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning](https://mlsanity.com/paper/2206.03200/) (2022)
- [SphereFed: Hyperspherical Federated Learning](https://mlsanity.com/paper/2207.09413/) (2022)
- [Vertical Federated Learning: A Structured Literature Review](https://mlsanity.com/paper/2212.00622/) (2022)
- [AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization](https://mlsanity.com/paper/2109.12519/) (2021)
- [ExPLoit: Extracting Private Labels in Split Learning](https://mlsanity.com/paper/2112.01299/) (2021)
- [Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering](https://mlsanity.com/paper/2210.14664/) (2022)
- [Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning](https://mlsanity.com/paper/2105.09540/) (2021)
- [Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies](https://mlsanity.com/paper/2109.03999/) (2021)
- [Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm](https://mlsanity.com/paper/2203.10329/) (2022)
- [Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties](https://mlsanity.com/paper/2303.18178/) (2023)
- [Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization](https://mlsanity.com/paper/2305.06279/) (2023)
- [A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective](https://mlsanity.com/paper/2402.03688/) (2024)
- [Efficient Batch Homomorphic Encryption for Vertically Federated XGBoost](https://mlsanity.com/paper/2112.04261/) (2021)
- [FedGBF: An efficient vertical federated learning framework via gradient boosting and bagging](https://mlsanity.com/paper/2204.00976/) (2022)
- [A Vertical Federated Learning Framework for Horizontally Partitioned Labels](https://mlsanity.com/paper/2106.10056/) (2021)
- [Differentially Private Vertical Federated Learning](https://mlsanity.com/paper/2211.06782/) (2022)
- [Secure Vertical Federated Learning Under Unreliable Connectivity](https://mlsanity.com/paper/2305.16794/) (2023)
- [GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data](https://mlsanity.com/paper/2303.09531/) (2023)
- [URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning](https://mlsanity.com/paper/2404.19582/) (2024)
- [Federated Granger Causality Learning for Interdependent Clients with State Space Representation](https://mlsanity.com/paper/2501.13890/) (2025)
- [Vertical federated learning based on DFP and BFGS](https://mlsanity.com/paper/2101.09428/) (2021)
- [SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree](https://mlsanity.com/paper/2110.10927/) (2021)
- [Privacy Against Inference Attacks in Vertical Federated Learning](https://mlsanity.com/paper/2207.11788/) (2022)
- [Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimization](https://mlsanity.com/paper/2306.16077/) (2023)

## 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.