mlsanityData to 7 Oct 2026

Federated Graph Learning -- A Position Paper

arXiv 2105.11099 ↗First posted 24 May 2021cs.LG cs.DC cs.NIcited by 34

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Abstract (arXiv)

Graph neural networks (GNN) have been successful in many fields, and derived various researches and applications in real industries. However, in some privacy sensitive scenarios (like finance, healthcare), training a GNN model centrally faces challenges due to the distributed data silos. Federated learning (FL) is a an emerging technique that can collaboratively train a shared model while keeping the data decentralized, which is a rational solution for distributed GNN training. We term it as federated graph learning (FGL). Although FGL has received increasing attention recently, the definition and challenges of FGL is still up in the air. In this position paper, we present a categorization to clarify it. Considering how graph data are distributed among clients, we propose four types of FGL: inter-graph FL, intra-graph FL and graph-structured FL, where intra-graph is further divided into horizontal and vertical FGL. For each type of FGL, we make a detailed discussion about the formulation and applications, and propose some potential challenges.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Builds on 22 of its 29 references that are in the map. First posted on arXiv 24 May 2021.

Builds on

22 of 29 references.

References · 22

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2017Inductive Representation Learning on Large Graphscited by 6,284
  5. 2019Advances and Open Problems in Federated Learningcited by 3,021
  6. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  7. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  8. 2019Federated Machine Learning: Concept and Applicationscited by 409
  9. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  10. 2020A Semi-supervised Graph Attentive Network for Financial Fraud Detectioncited by 150
  11. 2020Distilling Knowledge from Graph Convolutional Networkscited by 128
  12. 2021FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendationcited by 97
10 more
  1. 2020Heterogeneous Graph Neural Networks for Malicious Account Detectioncited by 95
  2. 2020Degree-Quant: Quantization-Aware Training for Graph Neural Networkscited by 93
  3. 2018GeniePath: Graph Neural Networks with Adaptive Receptive Pathscited by 66
  4. 2020GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphscited by 64
  5. 2020Multi-Participant Multi-Class Vertical Federated Learningcited by 47
  6. 2020FedE: Embedding Knowledge Graphs in Federated Settingcited by 46
  7. 2020ASFGNN: Automated Separated-Federated Graph Neural Networkcited by 36
  8. 2019Towards Federated Graph Learning for Collaborative Financial Crimes Detectioncited by 33
  9. 2020GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactionscited by 29
  10. 2020Federated Dynamic GNN with Secure Aggregationcited by 15

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 10; 2023: 6; 2024: 9; 2025: 7; 2026: 1.

34 papers.

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  1. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  2. 2022Federated Learning on Non-IID Graphs via Structural Knowledge Sharingcited by 88
  3. 2024A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challengescited by 54
  4. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  5. 2022GAP: Differentially Private Graph Neural Networks with Aggregation Perturbationcited by 46
  6. 2022Graph Lifelong Learning: A Surveycited by 30
  7. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  8. 2023A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applicationscited by 22
  9. 2022A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systemscited by 20
  10. 2025Ten Challenging Problems in Federated Foundation Modelscited by 20
  11. 2024FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preferencecited by 16
  12. 2022More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networkscited by 15
22 more
  1. 2021STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networkscited by 14
  2. 2022Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applicationscited by 14
  3. 2025Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglementcited by 14
  4. 2024Federated Graph Condensation with Information Bottleneck Principlescited by 8
  5. 2022An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learningcited by 7
  6. 2025S2FGL: Spatial Spectral Federated Graph Learningcited by 7
  7. 2025A Comprehensive Data-centric Overview of Federated Graph Learningcited by 7
  8. 2023Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approachcited by 5
  9. 2023Graph Mining for Cybersecurity: A Surveycited by 5
  10. 2024Federated Continual Graph Learningcited by 5
  11. 2024Gradient Inversion Attack on Graph Neural Networkscited by 4
  12. 2023GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Datacited by 3
  13. 2026Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approachcited by 2
  14. 2022Graph Federated Learning with Hidden Representation Sharingcited by 1
  15. 2024FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Frameworkcited by 1
  16. 2025VGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learningcited by 1
  17. 2025EC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compressioncited by 1
  18. 2025From Aggregation to Selection: User-Validated Distributed Social Recommendationcited by 1
  19. 2023A Framework for Exploring Federated Community Detectioncited by 0
  20. 2024Federated Hypergraph Learning with Local Differential Privacy: Toward Privacy-Aware Hypergraph Structure Completioncited by 0
  21. 2024Optimizing Federated Graph Learning with Inherent Structural Knowledge and Dual-Densely Connected GNNscited by 0
  22. 2024Federated Temporal Graph Clusteringcited by 0