# Distributed and Deep Vertical Federated Learning with Big Data

arXiv: https://arxiv.org/abs/2303.04574 · HTML: https://mlsanity.com/paper/2303.04574/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2023.

## Built on by (most cited first, 10 of 10)

- [Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization](https://mlsanity.com/paper/2310.15080/) (2023)
- [AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices](https://mlsanity.com/paper/2312.10935/) (2023)
- [Trustworthy Federated Learning: Privacy, Security, and Beyond](https://mlsanity.com/paper/2411.01583/) (2024)
- [Efficient Asynchronous Federated Learning with Sparsification and Quantization](https://mlsanity.com/paper/2312.15186/) (2023)
- [Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning](https://mlsanity.com/paper/2403.19178/) (2024)
- [Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data](https://mlsanity.com/paper/2408.05678/) (2024)
- [Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models](https://mlsanity.com/paper/2410.00131/) (2024)
- [SGDPO: Self-Guided Direct Preference Optimization for Language Model Alignment](https://mlsanity.com/paper/2505.12435/) (2025)
- [On Mask-based Image Set Desensitization with Recognition Support](https://mlsanity.com/paper/2312.08975/) (2023)
- [Efficient Federated Learning with Timely Update Dissemination](https://mlsanity.com/paper/2507.06031/) (2025)

## Abstract (arXiv)

In recent years, data are typically distributed in multiple organizations while the data security is becoming increasingly important. Federated Learning (FL), which enables multiple parties to collaboratively train a model without exchanging the raw data, has attracted more and more attention. Based on the distribution of data, FL can be realized in three scenarios, i.e., horizontal, vertical, and hybrid. In this paper, we propose to combine distributed machine learning techniques with Vertical FL and propose a Distributed Vertical Federated Learning (DVFL) approach. The DVFL approach exploits a fully distributed architecture within each party in order to accelerate the training process. In addition, we exploit Homomorphic Encryption (HE) to protect the data against honest-but-curious participants. We conduct extensive experimentation in a large-scale cluster environment and a cloud environment in order to show the efficiency and scalability of our proposed approach. The experiments demonstrate the good scalability of our approach and the significant efficiency advantage (up to 6.8 times with a single server and 15.1 times with multiple servers in terms of the training time) compared with baseline frameworks.