mlsanityData to 7 Oct 2026

Federated Graph Neural Networks: Overview, Techniques and Challenges

arXiv 2202.07256 ↗First posted 15 Feb 2022cs.DC cs.LGcited by 52

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Abstract (arXiv)

With its capability to deal with graph data, which is widely found in practical applications, graph neural networks (GNNs) have attracted significant research attention in recent years. As societies become increasingly concerned with the need for data privacy protection, GNNs face the need to adapt to this new normal. Besides, as clients in Federated Learning (FL) may have relationships, more powerful tools are required to utilize such implicit information to boost performance. This has led to the rapid development of the emerging research field of federated graph neural networks (FedGNNs). This promising interdisciplinary field is highly challenging for interested researchers to grasp. The lack of an insightful survey on this topic further exacerbates the entry difficulty. In this paper, we bridge this gap by offering a comprehensive survey of this emerging field. We propose a 2-dimensional taxonomy of the FedGNNs literature: 1) the main taxonomy provides a clear perspective on the integration of GNNs and FL by analyzing how GNNs enhance FL training as well as how FL assists GNNs training, and 2) the auxiliary taxonomy provides a view on how FedGNNs deal with heterogeneity across FL clients. Through discussions of key ideas, challenges, and limitations of existing works, we envision future research directions that can help build more robust, explainable, efficient, fair, inductive, and comprehensive FedGNNs.

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). Builds on 51 of its 128 references that are in the map. First posted on arXiv 15 Feb 2022.

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51 of 128 references.

References · 51

  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. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  6. 2018How Powerful are Graph Neural Networks?cited by 4,355
  7. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  8. 2019Advances and Open Problems in Federated Learningcited by 3,021
  9. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  10. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  11. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  12. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
38 more
  1. 2021Towards Personalized Federated Learningcited by 462
  2. 2017Graph Signal Processing: Overview, Challenges and Applicationscited by 409
  3. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 383
  4. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  5. 2018Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecastingcited by 145
  6. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  7. 2021Federated Graph Classification over Non-IID Graphscited by 113
  8. 2021FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendationcited by 97
  9. 2019Peer-to-peer Federated Learning on Graphscited by 85
  10. 2021Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modelingcited by 69
  11. 2020GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphscited by 64
  12. 2020Vertically Federated Graph Neural Network for Privacy-Preserving Node Classificationcited by 61
  13. 2021Federated Social Recommendation with Graph Neural Networkcited by 56
  14. 2020FedE: Embedding Knowledge Graphs in Federated Settingcited by 46
  15. 2021Differentially Private Federated Knowledge Graphs Embeddingcited by 45
  16. 2021Cluster-driven Graph Federated Learning over Multiple Domainscited by 43
  17. 2020ASFGNN: Automated Separated-Federated Graph Neural Networkcited by 36
  18. 2021LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interfacecited by 36
  19. 2021FedGL: Federated Graph Learning Framework with Global Self-Supervisioncited by 34
  20. 2021Federated Graph Learning -- A Position Papercited by 34
  21. 2021FedGraph: Federated Graph Learning with Intelligent Samplingcited by 34
  22. 2021SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networkscited by 30
  23. 2021A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularizationcited by 27
  24. 2021FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Predictioncited by 26
  25. 2020Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networkscited by 25
  26. 2022FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networkscited by 24
  27. 2021A Vertical Federated Learning Framework for Graph Convolutional Networkcited by 20
  28. 2021Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Settingcited by 19
  29. 2021Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learningcited by 17
  30. 2022Federated Learning with Heterogeneous Architectures using Graph HyperNetworkscited by 17
  31. 2020Federated Dynamic GNN with Secure Aggregationcited by 15
  32. 2020Distributed Training of Graph Convolutional Networkscited by 14
  33. 2021STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networkscited by 14
  34. 2021A Graph Federated Architecture with Privacy Preserving Learningcited by 10
  35. 2021Towards Representation Identical Privacy-Preserving Graph Neural Network via Split Learningcited by 10
  36. 2021FedMe: Federated Learning via Model Exchangecited by 10
  37. 2021FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Searchcited by 8
  38. 2021Power Allocation for Wireless Federated Learning using Graph Neural Networkscited by 7

50 most cited of 51 All 51 (JSON)

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Citing papers per year: 2022: 4; 2023: 11; 2024: 15; 2025: 14; 2026: 8.

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  1. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  2. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  3. 2024Advances and Open Challenges in Federated Foundation Modelscited by 33
  4. 2023A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applicationscited by 22
  5. 2023Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Reviewcited by 20
  6. 2024FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preferencecited by 16
  7. 2022GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Serverscited by 11
  8. 2023Privacy-Preserving Graph Machine Learning from Data to Computation: A Surveycited by 10
  9. 2023FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learningcited by 10
  10. 2022An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learningcited by 7
  11. 2023Edge-free but Structure-aware: Prototype-Guided Knowledge Distillation from GNNs to MLPscited by 7
  12. 2025S2FGL: Spatial Spectral Federated Graph Learningcited by 7
38 more
  1. 2025A Comprehensive Data-centric Overview of Federated Graph Learningcited by 7
  2. 2024FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scalercited by 6
  3. 2025Subgraph Federated Learning for Local Generalizationcited by 6
  4. 2023Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approachcited by 5
  5. 2024Survey of Graph Neural Network for Internet of Things and NextG Networkscited by 4
  6. 2025Subsampling Graphs with GNN Performance Guaranteescited by 4
  7. 2025Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systemscited by 4
  8. 2025Curriculum Guided Personalized Subgraph Federated Learningcited by 4
  9. 2024Decoupled Subgraph Federated Learningcited by 3
  10. 2024FedGraph: A Research Library and Benchmark for Federated Graph Learningcited by 3
  11. 2023Federated Neuro-Symbolic Learningcited by 2
  12. 2023Privacy-preserving design of graph neural networks with applications to vertical federated learningcited by 2
  13. 2024Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learningcited by 2
  14. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2
  15. 2024Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalizationcited by 2
  16. 2025Federated Learning from Molecules to Processes: A Perspectivecited by 2
  17. 2025Time Extrapolation with Graph Convolutional Autoencoder and Tensor Train Decompositioncited by 2
  18. 2023Hire When You Need to: Gradual Participant Recruitment for Auction-based Federated Learningcited by 1
  19. 2024Backdoor Attack on Vertical Federated Graph Neural Network Learningcited by 1
  20. 2025FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphscited by 1
  21. 2025Hyper-modal Imputation Diffusion Embedding with Dual-Distillation for Federated Multimodal Knowledge Graph Completioncited by 1
  22. 2022A Framework for Verifiable and Auditable Federated Anomaly Detectioncited by 0
  23. 2023The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learningcited by 0
  24. 2024Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic reviewcited by 0
  25. 2024Federated Graph Learning with Adaptive Importance-based Samplingcited by 0
  26. 2024Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Informationcited by 0
  27. 2024Focus Where It Matters: Graph Selective State Focused Attention Networkscited by 0
  28. 2024A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networkscited by 0
  29. 2025Efficient and Privacy-Preserved Link Prediction via Condensed Graphscited by 0
  30. 2025Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID Graphscited by 0
  31. 2025Who Owns This Sample: Cross-Client Membership Inference Attack in Federated Graph Neural Networkscited by 0
  32. 2025FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphscited by 0
  33. 2026FedGraph-VASP: Privacy-Preserving Federated Graph Learning with Post-Quantum Security for Cross-Institutional Anti-Money Launderingcited by 0
  34. 2026Fed-Listing: Federated Label Distribution Inference in Graph Neural Networkscited by 0
  35. 2026Federated Concept-Based Models: Interpretable models with distributed supervisioncited by 0
  36. 2026Fed-GAME: Personalized Federated Learning with Graph Attention Mixture-of-Experts For Time-Series Forecastingcited by 0
  37. 2026Federated Agentic AI for Wireless Networks: Fundamentals, Approaches, and Applicationscited by 0
  38. 2026Hide and Find: A Distributed Adversarial Attack on Federated Graph Learningcited by 0

50 most cited of 52 All 52 (JSON)