mlsanityData to 7 Oct 2026

Subgraph Federated Learning with Missing Neighbor Generation

arXiv 2106.13430 ↗First posted 25 Jun 2021cs.LG cs.SIcited by 132

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Abstract (arXiv)

Graphs have been widely used in data mining and machine learning due to their unique representation of real-world objects and their interactions. As graphs are getting bigger and bigger nowadays, it is common to see their subgraphs separately collected and stored in multiple local systems. Therefore, it is natural to consider the subgraph federated learning setting, where each local system holds a small subgraph that may be biased from the distribution of the whole graph. Hence, the subgraph federated learning aims to collaboratively train a powerful and generalizable graph mining model without directly sharing their graph data. In this work, towards the novel yet realistic setting of subgraph federated learning, we propose two major techniques: (1) FedSage, which trains a GraphSage model based on FedAvg to integrate node features, link structures, and task labels on multiple local subgraphs; (2) FedSage+, which trains a missing neighbor generator along FedSage to deal with missing links across local subgraphs. Empirical results on four real-world graph datasets with synthesized subgraph federated learning settings demonstrate the effectiveness and efficiency of our proposed techniques. At the same time, consistent theoretical implications are made towards their generalization ability on the global graphs.

Key facts. Cited by 132 papers in the map (data through 7 Oct 2026). Builds on 25 of its 49 references that are in the map. First posted on arXiv 25 Jun 2021.

Builds on

25 of 49 references.

References · 25

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  5. 2015Fast R-CNNcited by 2,930
  6. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  7. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  8. 2018On First-Order Meta-Learning Algorithmscited by 1,003
  9. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
  10. 2020Meta-Learning in Neural Networks: A Surveycited by 944
  11. 2018Adversarial Attacks on Neural Networks for Graph Datacited by 395
  12. 2020Graph Structure Learning for Robust Graph Neural Networkscited by 368
13 more
  1. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
  2. 2018Adversarial Attack on Graph Structured Datacited by 300
  3. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
  4. 2019Adversarial Attacks on Graph Neural Networks via Meta Learningcited by 261
  5. 2019Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernelscited by 160
  6. 2021FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendationcited by 97
  7. 2020Transfer Learning of Graph Neural Networks with Ego-graph Information Maximizationcited by 93
  8. 2019Certifiable Robustness and Robust Training for Graph Convolutional Networkscited by 59
  9. 2019DANE: Domain Adaptive Network Embeddingcited by 43
  10. 2019Federated Transfer Reinforcement Learning for Autonomous Drivingcited by 43
  11. 2020Secure Deep Graph Generation with Link Differential Privacycited by 24
  12. 2021PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformerscited by 23
  13. 2021Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networkscited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 22; 2023: 17; 2024: 34; 2025: 27; 2026: 29.

132 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. 2022Personalized Subgraph Federated Learningcited by 54
  4. 2022FederatedScope: A Flexible Federated Learning Platform for Heterogeneitycited by 53
  5. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  6. 2022GAP: Differentially Private Graph Neural Networks with Aggregation Perturbationcited by 46
  7. 2024FedGTA: Topology-aware Averaging for Federated Graph Learningcited by 44
  8. 2022FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learningcited by 43
  9. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 38
  10. 2023Inductive Graph Unlearningcited by 30
  11. 2024Federated Graph Semantic and Structural Learningcited by 30
  12. 2024FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learningcited by 27
38 more
  1. 2021FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Predictioncited by 26
  2. 2024AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneitycited by 25
  3. 2022FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networkscited by 24
  4. 2022Geometric Knowledge Distillation: Topology Compression for Graph Neural Networkscited by 22
  5. 2023Semi-decentralized Federated Ego Graph Learning for Recommendationcited by 22
  6. 2024OpenFGL: A Comprehensive Benchmark for Federated Graph Learningcited by 16
  7. 2024FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preferencecited by 16
  8. 2022Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Settingcited by 15
  9. 2022Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imagingcited by 14
  10. 2022Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applicationscited by 14
  11. 2024Deep Efficient Private Neighbor Generation for Subgraph Federated Learningcited by 14
  12. 2024Federated Graph Learning with Structure Proxy Alignmentcited by 14
  13. 2025Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglementcited by 14
  14. 2025GRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuningcited by 13
  15. 2022FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphscited by 12
  16. 2023Federated Learning over Coupled Graphscited by 12
  17. 2023FLGo: A Fully Customizable Federated Learning Platformcited by 12
  18. 2024Modeling Inter-Intra Heterogeneity for Graph Federated Learningcited by 12
  19. 2024Safety in Graph Machine Learning: Threats and Safeguardscited by 11
  20. 2026MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learningcited by 11
  21. 2023Privacy-Preserving Graph Machine Learning from Data to Computation: A Surveycited by 10
  22. 2023Towards Fair Graph Federated Learning via Incentive Mechanismscited by 8
  23. 2024Federated Graph Condensation with Information Bottleneck Principlescited by 8
  24. 2025FedBook: A Unified Federated Graph Foundation Codebook with Intra-domain and Inter-domain Knowledge Modelingcited by 8
  25. 2023FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networkscited by 7
  26. 2025S2FGL: Spatial Spectral Federated Graph Learningcited by 7
  27. 2025A Comprehensive Data-centric Overview of Federated Graph Learningcited by 7
  28. 2023Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devicescited by 6
  29. 2025Subgraph Federated Learning for Local Generalizationcited by 6
  30. 2025Rethinking Federated Graph Learning: A Data Condensation Perspectivecited by 6
  31. 2025Subgraph Federated Learning via Spectral Methodscited by 6
  32. 2024FedGT: Federated Node Classification with Scalable Graph Transformercited by 5
  33. 2024Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagationcited by 5
  34. 2024Federated Continual Graph Learningcited by 5
  35. 2023Personalized Federated Domain Adaptation for Item-to-Item Recommendationcited by 4
  36. 2023Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Networkcited by 4
  37. 2024HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learningcited by 4
  38. 2025Homophily Heterogeneity Matters in Graph Federated Learning: A Spectrum Sharing and Complementing Perspectivecited by 4

50 most cited of 132 All 132 (JSON)