# A Vertical Federated Learning Framework for Graph Convolutional Network

arXiv: https://arxiv.org/abs/2106.11593 · HTML: https://mlsanity.com/paper/2106.11593/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 22 Jun 2021.

## Builds on (most cited first, 15 of 15)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising](https://mlsanity.com/paper/1805.06530/) (2018)
- [Threats to Federated Learning: A Survey](https://mlsanity.com/paper/2003.02133/) (2020)
- [Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption](https://mlsanity.com/paper/1711.10677/) (2017)
- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)
- [FedE: Embedding Knowledge Graphs in Federated Setting](https://mlsanity.com/paper/2010.12882/) (2020)
- [Artificial Intelligence and Statistics](https://mlsanity.com/paper/1712.03779/) (2017)
- [Entity Resolution and Federated Learning get a Federated Resolution](https://mlsanity.com/paper/1803.04035/) (2018)
- [On Polynomial Approximations for Privacy-Preserving and Verifiable ReLU Networks](https://mlsanity.com/paper/2011.05530/) (2020)
- [Secure Social Recommendation based on Secret Sharing](https://mlsanity.com/paper/2002.02088/) (2020)
- [Privacy Partitioning: Protecting User Data During the Deep Learning Inference Phase](https://mlsanity.com/paper/1812.02863/) (2018)

## Built on by (most cited first, 21 of 21)

- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [Trustworthy Graph Neural Networks: Aspects, Methods and Trends](https://mlsanity.com/paper/2205.07424/) (2022)
- [Federated Graph Neural Networks: Overview, Techniques and Challenges](https://mlsanity.com/paper/2202.07256/) (2022)
- [GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation](https://mlsanity.com/paper/2203.00949/) (2022)
- [Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI](https://mlsanity.com/paper/2111.06061/) (2021)
- [A Survey on Vertical Federated Learning: From a Layered Perspective](https://mlsanity.com/paper/2304.01829/) (2023)
- [A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications](https://mlsanity.com/paper/2308.16375/) (2023)
- [A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems](https://mlsanity.com/paper/2206.04149/) (2022)
- [Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning](https://mlsanity.com/paper/2110.06468/) (2021)
- [Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications](https://mlsanity.com/paper/2207.11812/) (2022)
- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [Vertical Federated Learning: Taxonomies, Threats, and Prospects](https://mlsanity.com/paper/2302.01550/) (2023)
- [GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data](https://mlsanity.com/paper/2303.09531/) (2023)
- [FedGraph: A Research Library and Benchmark for Federated Graph Learning](https://mlsanity.com/paper/2410.06340/) (2024)
- [P4GCN: Vertical Federated Social Recommendation with Privacy-Preserving Two-Party Graph Convolution Network](https://mlsanity.com/paper/2410.13905/) (2024)
- [Label Inference Attacks against Node-level Vertical Federated GNNs](https://mlsanity.com/paper/2308.02465/) (2023)
- [VGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learning](https://mlsanity.com/paper/2502.16793/) (2025)
- [Federated Graph Representation Learning using Self-Supervision](https://mlsanity.com/paper/2210.15120/) (2022)
- [A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structures](https://mlsanity.com/paper/2403.16004/) (2024)
- [Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning](https://mlsanity.com/paper/2411.02809/) (2024)
- [From Euclidean to Graph-Structured Data: A Survey of Collaborative Learning](https://mlsanity.com/paper/2609.02984/) (2026)

## Abstract (arXiv)

Recently, Graph Neural Network (GNN) has achieved remarkable success in various real-world problems on graph data. However in most industries, data exists in the form of isolated islands and the data privacy and security is also an important issue. In this paper, we propose FedVGCN, a federated GCN learning paradigm for privacy-preserving node classification task under data vertically partitioned setting, which can be generalized to existing GCN models. Specifically, we split the computation graph data into two parts. For each iteration of the training process, the two parties transfer intermediate results to each other under homomorphic encryption. We conduct experiments on benchmark data and the results demonstrate the effectiveness of FedVGCN in the case of GraphSage.