mlsanityData to 14 Oct 2026

Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification

arXiv 2005.11903 ↗First posted 25 May 2020cs.LG cs.CR stat.MLcited by 61

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Abstract (arXiv)

Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between different nodes. High-performance GNN models always depend on both rich features and complete edge information in graph. However, such information could possibly be isolated by different data holders in practice, which is the so-called data isolation problem. To solve this problem, in this paper, we propose VFGNN, a federated GNN learning paradigm for privacy-preserving node classification task under data vertically partitioned setting, which can be generalized to existing GNN models. Specifically, we split the computation graph into two parts. We leave the private data (i.e., features, edges, and labels) related computations on data holders, and delegate the rest of computations to a semi-honest server. We also propose to apply differential privacy to prevent potential information leakage from the server. We conduct experiments on three benchmarks and the results demonstrate the effectiveness of VFGNN.

Key facts. Cited by 61 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 May 2020.

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Citing papers per year: 2021: 5; 2022: 19; 2023: 12; 2024: 14; 2025: 7; 2026: 4.

61 papers.

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  1. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  2. 2021Federated Graph Classification over Non-IID Graphscited by 114
  3. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 108
  4. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 89
  5. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 66
  6. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  7. 2022A Survey on Heterogeneous Federated Learningcited by 42
  8. 2021Releasing Graph Neural Networks with Differential Privacy Guaranteescited by 32
  9. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  10. 2023Reducing Communication for Split Learning by Randomized Top-k Sparsificationcited by 22
  11. 2023A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applicationscited by 22
  12. 2022A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protectioncited by 20
38 more
  1. 2022A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systemscited by 20
  2. 2021Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learningcited by 17
  3. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  4. 2022More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networkscited by 15
  5. 2022Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applicationscited by 14
  6. 2024Deep Efficient Private Neighbor Generation for Subgraph Federated Learningcited by 14
  7. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  8. 2022Differentially Private Graph Classification with GNNscited by 12
  9. 2023Federated Learning over Coupled Graphscited by 12
  10. 2024Safety in Graph Machine Learning: Threats and Safeguardscited by 11
  11. 2022SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Servicecited by 10
  12. 2022FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacycited by 9
  13. 2021GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategycited by 8
  14. 2023Vertical Federated Learning: Taxonomies, Threats, and Prospectscited by 8
  15. 2023HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learningcited by 8
  16. 2022A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacycited by 7
  17. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  18. 2025A Comprehensive Data-centric Overview of Federated Graph Learningcited by 7
  19. 2022Cross-Network Social User Embedding with Hybrid Differential Privacy Guaranteescited by 5
  20. 2022Hijack Vertical Federated Learning Models As One Partycited by 5
  21. 2023VertexSerum: Poisoning Graph Neural Networks for Link Inferencecited by 5
  22. 2023Federated Learning on Non-iid Data via Local and Global Distillationcited by 4
  23. 2022Protecting Split Learning by Potential Energy Losscited by 3
  24. 2023GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Datacited by 3
  25. 2024Federated Graph Learning with Graphless Clientscited by 3
  26. 2025Tree-based Models for Vertical Federated Learning: A Surveycited by 3
  27. 2025Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defensescited by 3
  28. 2023Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimizationcited by 2
  29. 2023Privacy-preserving design of graph neural networks with applications to vertical federated learningcited by 2
  30. 2024P4GCN: Vertical Federated Social Recommendation with Privacy-Preserving Two-Party Graph Convolution Networkcited by 2
  31. 2023Label Inference Attacks against Node-level Vertical Federated GNNscited by 1
  32. 2024On provable privacy vulnerabilities of graph representationscited by 1
  33. 2024One Node Per User: Node-Level Federated Learning for Graph Neural Networkscited by 1
  34. 2024Backdoor Attack on Vertical Federated Graph Neural Network Learningcited by 1
  35. 2025VGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learningcited by 1
  36. 2022Scalable and Sparsity-Aware Privacy-Preserving K-means Clustering with Application to Fraud Detectioncited by 0
  37. 2022Federated Graph Representation Learning using Self-Supervisioncited by 0
  38. 2024A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structurescited by 0

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