mlsanityData to 14 Oct 2026

Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications

arXiv 2207.11812 ↗First posted 24 Jul 2022cs.LGcited by 14

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Abstract (arXiv)

Graph machine learning has gained great attention in both academia and industry recently. Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are trained over massive graph data. However, in many real-world scenarios, such as hospitalization prediction in healthcare systems, the graph data is usually stored at multiple data owners and cannot be directly accessed by any other parties due to privacy concerns and regulation restrictions. Federated Graph Machine Learning (FGML) is a promising solution to tackle this challenge by training graph machine learning models in a federated manner. In this survey, we conduct a comprehensive review of the literature in FGML. Specifically, we first provide a new taxonomy to divide the existing problems in FGML into two settings, namely, FL with structured data and structured FL. Then, we review the mainstream techniques in each setting and elaborate on how they address the challenges under FGML. In addition, we summarize the real-world applications of FGML from different domains and introduce open graph datasets and platforms adopted in FGML. Finally, we present several limitations in the existing studies with promising research directions in this field.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 85 of its 151 references that are in the map. First posted on arXiv 24 Jul 2022.

Builds on

85 of 151 references.

References · 85

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38 more
  1. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 6; 2025: 2; 2026: 1.

14 papers.

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  1. 2024FedGTA: Topology-aware Averaging for Federated Graph Learningcited by 44
  2. 2024FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preferencecited by 16
  3. 2024Federated Graph Learning with Structure Proxy Alignmentcited by 14
  4. 2022GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Serverscited by 11
  5. 2024Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Trainingcited by 10
  6. 2023Federated Few-shot Learningcited by 9
  7. 2023Vertical Federated Learning: Taxonomies, Threats, and Prospectscited by 8
  8. 2023Federated Knowledge Graph Completion via Latent Embedding Sharing and Tensor Factorizationcited by 5
  9. 2024Federated Continual Graph Learningcited by 5
  10. 2024Machine Learning on Dynamic Graphs: A Survey on Applicationscited by 4
  11. 2023Survey of Federated Learning Models for Spatial-Temporal Mobility Applicationscited by 2
  12. 2025FedAGHN: Personalized Federated Learning with Attentive Graph HyperNetworkscited by 2
2 more
  1. 2025FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcastingcited by 0
  2. 2026Communication-efficient Federated Graph Classification via Generative Diffusion Modelingcited by 0