# FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning

arXiv: https://arxiv.org/abs/2302.10417 · HTML: https://mlsanity.com/paper/2302.10417/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 37 references that are in the map.
- First posted on arXiv 21 Feb 2023.

## Builds on (most cited first, 12 of 12)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Learning Sparse Neural Networks through L0 Regularization](https://mlsanity.com/paper/1712.01312/) (2017)
- [Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption](https://mlsanity.com/paper/1711.10677/) (2017)
- [SecureBoost: A Lossless Federated Learning Framework](https://mlsanity.com/paper/1901.08755/) (2019)
- [VAFL: a Method of Vertical Asynchronous Federated Learning](https://mlsanity.com/paper/2007.06081/) (2020)
- [Reducing Reparameterization Gradient Variance](https://mlsanity.com/paper/1705.07880/) (2017)
- [A Communication Efficient Collaborative Learning Framework for Distributed Features](https://mlsanity.com/paper/1912.11187/) (2019)
- [Kernel Feature Selection via Conditional Covariance Minimization](https://mlsanity.com/paper/1707.01164/) (2017)
- [Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification](https://mlsanity.com/paper/2108.06493/) (2021)
- [Additively Homomorphical Encryption based Deep Neural Network for Asymmetrically Collaborative Machine Learning](https://mlsanity.com/paper/2007.06849/) (2020)
- [Privacy-Preserving Feature Selection with Secure Multiparty Computation](https://mlsanity.com/paper/2102.03517/) (2021)
- [Residue-based Label Protection Mechanisms in Vertical Logistic Regression](https://mlsanity.com/paper/2205.04166/) (2022)

## Built on by (most cited first, 15 of 15)

- [Advances and Open Challenges in Federated Foundation Models](https://mlsanity.com/paper/2404.15381/) (2024)
- [LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning](https://mlsanity.com/paper/2305.02219/) (2023)
- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [Towards Interpretable Federated Learning](https://mlsanity.com/paper/2302.13473/) (2023)
- [Vertical Federated Unlearning via Backdoor Certification](https://mlsanity.com/paper/2412.11476/) (2024)
- [A Survey on Contribution Evaluation in Vertical Federated Learning](https://mlsanity.com/paper/2405.02364/) (2024)
- [Trustworthy Federated Learning: A Survey](https://mlsanity.com/paper/2305.11537/) (2023)
- [FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregation](https://mlsanity.com/paper/2312.00102/) (2023)
- [Fairness-Aware Job Scheduling for Multi-Job Federated Learning](https://mlsanity.com/paper/2401.02740/) (2024)
- [TabVFL: Improving Latent Representation in Vertical Federated Learning](https://mlsanity.com/paper/2404.17990/) (2024)
- [Embedded Federated Feature Selection with Dynamic Sparse Training: Balancing Accuracy-Cost Tradeoffs](https://mlsanity.com/paper/2504.05245/) (2025)
- [OPUS-VFL: Incentivizing Optimal Privacy-Utility Tradeoffs in Vertical Federated Learning](https://mlsanity.com/paper/2504.15995/) (2025)
- [ICAFS: Inter-Client-Aware Feature Selection for Vertical Federated Learning](https://mlsanity.com/paper/2504.10851/) (2025)
- [PQFed: A Privacy-Preserving Quality-Controlled Federated Learning Framework](https://mlsanity.com/paper/2509.21704/) (2025)
- [Privacy-Preserving Feature Valuation in Vertical Federated Learning Using Shapley-CMI and PSI Permutation](https://mlsanity.com/paper/2512.14767/) (2025)

## Abstract (arXiv)

Vertical Federated Learning (VFL) enables multiple data owners, each holding a different subset of features about largely overlapping sets of data sample(s), to jointly train a useful global model. Feature selection (FS) is important to VFL. It is still an open research problem as existing FS works designed for VFL either assumes prior knowledge on the number of noisy features or prior knowledge on the post-training threshold of useful features to be selected, making them unsuitable for practical applications. To bridge this gap, we propose the Federated Stochastic Dual-Gate based Feature Selection (FedSDG-FS) approach. It consists of a Gaussian stochastic dual-gate to efficiently approximate the probability of a feature being selected, with privacy protection through Partially Homomorphic Encryption without a trusted third-party. To reduce overhead, we propose a feature importance initialization method based on Gini impurity, which can accomplish its goals with only two parameter transmissions between the server and the clients. Extensive experiments on both synthetic and real-world datasets show that FedSDG-FS significantly outperforms existing approaches in terms of achieving accurate selection of high-quality features as well as building global models with improved performance.