mlsanityData to 7 Oct 2026

A Vertical Federated Learning Framework for Graph Convolutional Network

arXiv 2106.11593 ↗First posted 22 Jun 2021cs.LG cs.AIcited by 20

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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.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 15 of its 37 references that are in the map. First posted on arXiv 22 Jun 2021.

Builds on

15 of 37 references.

References · 15

  1. 2017Graph Attention Networkscited by 8,854
  2. 2017Inductive Representation Learning on Large Graphscited by 6,284
  3. 2019Towards Federated Learning at Scale: System Designcited by 826
  4. 2013Network In Networkcited by 600
  5. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  6. 2018Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoisingcited by 216
  7. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  8. 2020Threats to Federated Learning: A Surveycited by 171
  9. 2017A Survey on Homomorphic Encryption Schemes: Theory and Implementationcited by 154
  10. 2020FedE: Embedding Knowledge Graphs in Federated Settingcited by 46
  11. 2017Artificial Intelligence and Statisticscited by 43
  12. 2018Entity Resolution and Federated Learning get a Federated Resolutioncited by 23
3 more
  1. 2020On Polynomial Approximations for Privacy-Preserving and Verifiable ReLU Networkscited by 19
  2. 2020Secure Social Recommendation based on Secret Sharingcited by 12
  3. 2018Privacy Partitioning: Protecting User Data During the Deep Learning Inference Phasecited by 8

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Cited by year

Citing papers per year: 2021: 2; 2022: 7; 2023: 5; 2024: 5; 2025: 1.

20 papers.

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  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  2. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  3. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  4. 2022GAP: Differentially Private Graph Neural Networks with Aggregation Perturbationcited by 46
  5. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 38
  6. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  7. 2023A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applicationscited by 22
  8. 2022A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systemscited by 20
  9. 2021Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learningcited by 17
  10. 2022Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applicationscited by 14
  11. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  12. 2023Vertical Federated Learning: Taxonomies, Threats, and Prospectscited by 8
8 more
  1. 2023GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Datacited by 3
  2. 2024FedGraph: A Research Library and Benchmark for Federated Graph Learningcited by 3
  3. 2024P4GCN: Vertical Federated Social Recommendation with Privacy-Preserving Two-Party Graph Convolution Networkcited by 2
  4. 2023Label Inference Attacks against Node-level Vertical Federated GNNscited by 1
  5. 2025VGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learningcited by 1
  6. 2022Federated Graph Representation Learning using Self-Supervisioncited by 0
  7. 2024A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structurescited by 0
  8. 2024Query-Efficient Adversarial Attack Against Vertical Federated Graph Learningcited by 0