# Secure Vertical Federated Learning Under Unreliable Connectivity

arXiv: https://arxiv.org/abs/2305.16794 · HTML: https://mlsanity.com/paper/2305.16794/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 36 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 23 of 23)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [iDLG: Improved Deep Leakage from Gradients](https://mlsanity.com/paper/2001.02610/) (2020)
- [See through Gradients: Image Batch Recovery via GradInversion](https://mlsanity.com/paper/2104.07586/) (2021)
- [Practical Secure Aggregation for Federated Learning on User-Held Data](https://mlsanity.com/paper/1611.04482/) (2016)
- [Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption](https://mlsanity.com/paper/1711.10677/) (2017)
- [VAFL: a Method of Vertical Asynchronous Federated Learning](https://mlsanity.com/paper/2007.06081/) (2020)
- [CAFE: Catastrophic Data Leakage in Vertical Federated Learning](https://mlsanity.com/paper/2110.15122/) (2021)
- [LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2109.14236/) (2021)
- [Vertical Federated Learning: Challenges, Methodologies and Experiments](https://mlsanity.com/paper/2202.04309/) (2022)
- [ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity](https://mlsanity.com/paper/2208.02507/) (2022)
- [A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression](https://mlsanity.com/paper/1912.00513/) (2019)
- [BlindFL: Vertical Federated Machine Learning without Peeking into Your Data](https://mlsanity.com/paper/2206.07975/) (2022)
- [Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability](https://mlsanity.com/paper/2111.10934/) (2021)
- [Label Leakage and Protection from Forward Embedding in Vertical Federated Learning](https://mlsanity.com/paper/2203.01451/) (2022)
- [Asymmetrical Vertical Federated Learning](https://mlsanity.com/paper/2004.07427/) (2020)
- [Large-Scale Secure XGB for Vertical Federated Learning](https://mlsanity.com/paper/2005.08479/) (2020)
- [Additively Homomorphical Encryption based Deep Neural Network for Asymmetrically Collaborative Machine Learning](https://mlsanity.com/paper/2007.06849/) (2020)
- [A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning](https://mlsanity.com/paper/2209.03885/) (2022)
- [Scheduling for Ground-Assisted Federated Learning in LEO Satellite Constellations](https://mlsanity.com/paper/2206.01952/) (2022)
- [Secure Forward Aggregation for Vertical Federated Neural Networks](https://mlsanity.com/paper/2207.00165/) (2022)
- [Protecting Split Learning by Potential Energy Loss](https://mlsanity.com/paper/2210.09617/) (2022)

## Built on by (most cited first, 4 of 4)

- [A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective](https://mlsanity.com/paper/2402.03688/) (2024)
- [Speed Up Federated Learning in Heterogeneous Environment: A Dynamic Tiering Approach](https://mlsanity.com/paper/2312.05642/) (2023)
- [Just a Simple Transformation is Enough for Data Protection in Vertical Federated Learning](https://mlsanity.com/paper/2412.11689/) (2024)
- [Secure and Privacy-Preserving Vertical Federated Learning](https://mlsanity.com/paper/2604.13474/) (2026)

## Abstract (arXiv)

Most work in privacy-preserving federated learning (FL) has focused on horizontally partitioned datasets where clients hold the same features and train complete client-level models independently. However, individual data points are often scattered across different institutions, known as clients, in vertical FL (VFL) settings. Addressing this category of FL necessitates the exchange of intermediate outputs and gradients among participants, resulting in potential privacy leakage risks and slow convergence rates. Additionally, in many real-world scenarios, VFL training also faces the acute issue of client stragglers and drop-outs, a serious challenge that can significantly hinder the training process but has been largely overlooked in existing studies. In this work, we present vFedSec, a first dropout-tolerant VFL protocol, which can support the most generalized vertical framework. It achieves secure and efficient model training by using an innovative Secure Layer alongside an embedding-padding technique. We provide theoretical proof that our design attains enhanced security while maintaining training performance. Empirical results from extensive experiments also demonstrate vFedSec is robust to client dropout and provides secure training with negligible computation and communication overhead. Compared to widely adopted homomorphic encryption (HE) methods, our approach achieves a remarkable > 690x speedup and reduces communication costs significantly by > 9.6x.