# Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data

arXiv: https://arxiv.org/abs/2008.06197 · HTML: https://mlsanity.com/paper/2008.06197/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2020.

## Built on by (most cited first, 30 of 36)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Vertical Federated Learning: Challenges, Methodologies and Experiments](https://mlsanity.com/paper/2202.04309/) (2022)
- [Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating](https://mlsanity.com/paper/2103.00958/) (2021)
- [Trading Off Privacy, Utility and Efficiency in Federated Learning](https://mlsanity.com/paper/2209.00230/) (2022)
- [FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data](https://mlsanity.com/paper/2103.03918/) (2021)
- [Membership Inference Attacks and Defenses in Federated Learning: A Survey](https://mlsanity.com/paper/2412.06157/) (2024)
- [A Survey on Vertical Federated Learning: From a Layered Perspective](https://mlsanity.com/paper/2304.01829/) (2023)
- [AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization](https://mlsanity.com/paper/2109.12519/) (2021)
- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning](https://mlsanity.com/paper/2105.09540/) (2021)
- [Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM](https://mlsanity.com/paper/2207.10226/) (2022)
- [Differentially Private Vertical Federated Clustering](https://mlsanity.com/paper/2208.01700/) (2022)
- [An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization](https://mlsanity.com/paper/2105.05717/) (2021)
- [Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies](https://mlsanity.com/paper/2109.03999/) (2021)
- [Continual Horizontal Federated Learning for Heterogeneous Data](https://mlsanity.com/paper/2203.02108/) (2022)
- [Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond](https://mlsanity.com/paper/2207.09087/) (2022)
- [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)
- [An Efficient and Robust System for Vertically Federated Random Forest](https://mlsanity.com/paper/2201.10761/) (2022)
- [VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks](https://mlsanity.com/paper/2307.02040/) (2023)
- [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)
- [Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform](https://mlsanity.com/paper/2107.04129/) (2021)
- [Robust and Communication-Efficient Federated Domain Adaptation via Random Features](https://mlsanity.com/paper/2311.04686/) (2023)
- [Tree-based Models for Vertical Federated Learning: A Survey](https://mlsanity.com/paper/2504.02285/) (2025)
- [A Vertical Federated Learning Method For Multi-Institutional Credit Scoring: MICS](https://mlsanity.com/paper/2111.09038/) (2021)
- [Blind Federated Learning without initial model](https://mlsanity.com/paper/2404.16180/) (2024)
- [DVFL: A Vertical Federated Learning Method for Dynamic Data](https://mlsanity.com/paper/2111.03341/) (2021)

## Abstract (arXiv)

In a lot of real-world data mining and machine learning applications, data are provided by multiple providers and each maintains private records of different feature sets about common entities. It is challenging to train these vertically partitioned data effectively and efficiently while keeping data privacy for traditional data mining and machine learning algorithms. In this paper, we focus on nonlinear learning with kernels, and propose a federated doubly stochastic kernel learning (FDSKL) algorithm for vertically partitioned data. Specifically, we use random features to approximate the kernel mapping function and use doubly stochastic gradients to update the solutions, which are all computed federatedly without the disclosure of data. Importantly, we prove that FDSKL has a sublinear convergence rate, and can guarantee the data security under the semi-honest assumption. Extensive experimental results on a variety of benchmark datasets show that FDSKL is significantly faster than state-of-the-art federated learning methods when dealing with kernels, while retaining the similar generalization performance.