mlsanityData to 14 Oct 2026

Federated Graph Classification over Non-IID Graphs

arXiv 2106.13423 ↗First posted 25 Jun 2021cs.LG cs.AI cs.DCcited by 114

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Abstract (arXiv)

Federated learning has emerged as an important paradigm for training machine learning models in different domains. For graph-level tasks such as graph classification, graphs can also be regarded as a special type of data samples, which can be collected and stored in separate local systems. Similar to other domains, multiple local systems, each holding a small set of graphs, may benefit from collaboratively training a powerful graph mining model, such as the popular graph neural networks (GNNs). To provide more motivation towards such endeavors, we analyze real-world graphs from different domains to confirm that they indeed share certain graph properties that are statistically significant compared with random graphs. However, we also find that different sets of graphs, even from the same domain or same dataset, are non-IID regarding both graph structures and node features. To handle this, we propose a graph clustered federated learning (GCFL) framework that dynamically finds clusters of local systems based on the gradients of GNNs, and theoretically justify that such clusters can reduce the structure and feature heterogeneity among graphs owned by the local systems. Moreover, we observe the gradients of GNNs to be rather fluctuating in GCFL which impedes high-quality clustering, and design a gradient sequence-based clustering mechanism based on dynamic time warping (GCFL+). Extensive experimental results and in-depth analysis demonstrate the effectiveness of our proposed frameworks.

Key facts. Cited by 114 papers in the map (data through 14 Oct 2026). Builds on 35 of its 63 references that are in the map. First posted on arXiv 25 Jun 2021.

Builds on

35 of 63 references.

References · 35

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23 more
  1. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  2. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  3. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  4. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  5. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 312
  6. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 283
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 23; 2023: 14; 2024: 29; 2025: 24; 2026: 19.

114 papers.

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  1. 2022Federated Learning from Pre-Trained Models: A Contrastive Learning Approachcited by 142
  2. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  3. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 89
  4. 2022Federated Learning on Non-IID Graphs via Structural Knowledge Sharingcited by 88
  5. 2022Personalized Subgraph Federated Learningcited by 54
  6. 2022FederatedScope: A Flexible Federated Learning Platform for Heterogeneitycited by 53
  7. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  8. 2022GAP: Differentially Private Graph Neural Networks with Aggregation Perturbationcited by 46
  9. 2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
  10. 2024FedGTA: Topology-aware Averaging for Federated Graph Learningcited by 44
  11. 2022FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learningcited by 43
  12. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 39
38 more
  1. 2024Federated Graph Semantic and Structural Learningcited by 30
  2. 2024FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learningcited by 27
  3. 2024AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneitycited by 25
  4. 2022DELTA: Diverse Client Sampling for Fasting Federated Learningcited by 24
  5. 2023Semi-decentralized Federated Ego Graph Learning for Recommendationcited by 22
  6. 2022A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protectioncited by 20
  7. 2021Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Settingcited by 19
  8. 2021Source Free Unsupervised Graph Domain Adaptationcited by 18
  9. 2021FedDAG: Federated DAG Structure Learningcited by 17
  10. 2023Unraveling Privacy Risks of Individual Fairness in Graph Neural Networkscited by 16
  11. 2024OpenFGL: A Comprehensive Benchmark for Federated Graph Learningcited by 16
  12. 2024FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preferencecited by 16
  13. 2022Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applicationscited by 14
  14. 2024Deep Efficient Private Neighbor Generation for Subgraph Federated Learningcited by 14
  15. 2024Federated Graph Learning with Structure Proxy Alignmentcited by 14
  16. 2025Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglementcited by 14
  17. 2022FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphscited by 12
  18. 2024Modeling Inter-Intra Heterogeneity for Graph Federated Learningcited by 12
  19. 2022GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Serverscited by 11
  20. 2024Safety in Graph Machine Learning: Threats and Safeguardscited by 11
  21. 2026MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learningcited by 11
  22. 2023Privacy-Preserving Graph Machine Learning from Data to Computation: A Surveycited by 10
  23. 2023Towards Fair Graph Federated Learning via Incentive Mechanismscited by 8
  24. 2024Federated Graph Condensation with Information Bottleneck Principlescited by 8
  25. 2025FedBook: A Unified Federated Graph Foundation Codebook with Intra-domain and Inter-domain Knowledge Modelingcited by 8
  26. 2023EvoluNet: Advancing Dynamic Non-IID Transfer Learning on Graphscited by 7
  27. 2023FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networkscited by 7
  28. 2024Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clientscited by 7
  29. 2025S2FGL: Spatial Spectral Federated Graph Learningcited by 7
  30. 2025A Comprehensive Data-centric Overview of Federated Graph Learningcited by 7
  31. 2023State of the Art and Potentialities of Graph-level Learningcited by 6
  32. 2023Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devicescited by 6
  33. 2025Federated Learning for Medical Image Classification: A Comprehensive Benchmarkcited by 6
  34. 2026FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detectioncited by 6
  35. 2024FedGT: Federated Node Classification with Scalable Graph Transformercited by 5
  36. 2024Federated Continual Graph Learningcited by 5
  37. 2025GRAIN: Exact Graph Reconstruction from Gradientscited by 5
  38. 2023Personalized Federated Domain Adaptation for Item-to-Item Recommendationcited by 4

50 most cited of 114 All 114 (JSON)