mlsanityData to 14 Oct 2026

A Comprehensive Data-centric Overview of Federated Graph Learning

arXiv 2507.16541 ↗First posted 22 Jul 2025cs.LG cs.AI cs.SIcited by 7

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Abstract (arXiv)

In the era of big data applications, Federated Graph Learning (FGL) has emerged as a prominent solution that reconcile the tradeoff between optimizing the collective intelligence between decentralized datasets holders and preserving sensitive information to maximum. Existing FGL surveys have contributed meaningfully but largely focus on integrating Federated Learning (FL) and Graph Machine Learning (GML), resulting in early stage taxonomies that emphasis on methodology and simulated scenarios. Notably, a data centric perspective, which systematically examines FGL methods through the lens of data properties and usage, remains unadapted to reorganize FGL research, yet it is critical to assess how FGL studies manage to tackle data centric constraints to enhance model performances. This survey propose a two-level data centric taxonomy: Data Characteristics, which categorizes studies based on the structural and distributional properties of datasets used in FGL, and Data Utilization, which analyzes the training procedures and techniques employed to overcome key data centric challenges. Each taxonomy level is defined by three orthogonal criteria, each representing a distinct data centric configuration. Beyond taxonomy, this survey examines FGL integration with Pretrained Large Models, showcases realistic applications, and highlights future direction aligned with emerging trends in GML.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 110 of its 206 references that are in the map. First posted on arXiv 22 Jul 2025.

Builds on

110 of 206 references.

References · 110

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  4. 2017Inductive Representation Learning on Large Graphscited by 6,320
  5. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  6. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  7. 2017PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruningcited by 898
  8. 2018Link Prediction Based on Graph Neural Networkscited by 832
  9. 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
  10. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  11. 2020MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embeddingcited by 304
  12. 2023One for All: Towards Training One Graph Model for All Classification Taskscited by 243
38 more
  1. 2023GraphGPT: Graph Instruction Tuning for Large Language Modelscited by 226
  2. 2023Representation Learning with Large Language Models for Recommendationcited by 155
  3. 2023FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learningcited by 149
  4. 2023Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learningcited by 133
  5. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  6. 2021Federated Graph Classification over Non-IID Graphscited by 114
  7. 2023GraphLLM: Boosting Graph Reasoning Ability of Large Language Modelcited by 107
  8. 2021FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendationcited by 98
  9. 2020Overcoming Catastrophic Forgetting in Graph Neural Networkscited by 95
  10. 2019Graph Convolutional Networks with EigenPoolingcited by 94
  11. 2023SLoRA: Federated Parameter Efficient Fine-Tuning of Language Modelscited by 93
  12. 2022Federated Learning on Non-IID Graphs via Structural Knowledge Sharingcited by 88
  13. 2023InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discoverycited by 86
  14. 2019Peer-to-peer Federated Learning on Graphscited by 85
  15. 2024OpenGraph: Towards Open Graph Foundation Modelscited by 78
  16. 2024FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Modelcited by 74
  17. 2021Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modelingcited by 69
  18. 2024UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphscited by 66
  19. 2023Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimizationcited by 65
  20. 2020Locally Private Graph Neural Networkscited by 64
  21. 2020GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphscited by 64
  22. 2023FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Modelscited by 63
  23. 2020Vertically Federated Graph Neural Network for Privacy-Preserving Node Classificationcited by 61
  24. 2024LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddingscited by 61
  25. 2021Federated Social Recommendation with Graph Neural Networkcited by 56
  26. 2022Personalized Subgraph Federated Learningcited by 54
  27. 2022Multimodal learning with graphscited by 54
  28. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  29. 2022Certified Graph Unlearningcited by 46
  30. 2024HiGPT: Heterogeneous Graph Language Modelcited by 45
  31. 2021Cluster-driven Graph Federated Learning over Multiple Domainscited by 44
  32. 2024FedGTA: Topology-aware Averaging for Federated Graph Learningcited by 44
  33. 2023Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearningcited by 40
  34. 2021Federated Graph Learning -- A Position Papercited by 35
  35. 2021FedGL: Federated Graph Learning Framework with Global Self-Supervisioncited by 34
  36. 2021FedGraph: Federated Graph Learning with Intelligent Samplingcited by 34
  37. 2019Towards Federated Graph Learning for Collaborative Financial Crimes Detectioncited by 33
  38. 2020Decentralized Federated Learning via SGD over Wireless D2D Networkscited by 33

50 most cited of 110 All 110 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 6.

7 papers.

  1. 2026TMTE: Effective Multimodal Graph Learning with Task-aware Modality and Topology Co-evolutioncited by 3
  2. 2026STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learningcited by 3
  3. 2025Dissecting Federated-Graph Aggregation under Domain Shift: Importance-Aware Aggregation via Empirical Analysiscited by 0
  4. 2026BoostFGL: Boosting Fairness in Federated Graph Learningcited by 0
  5. 2026DANCE: Dynamic, Available, Neighbor-gated Condensation for Federated Text-Attributed Graphscited by 0
  6. 2026MMFGU: Multimodal Federated Graph Unlearningcited by 0
  7. 2026Towards Adaptive Federated Graph Clustering: A Global Community-aware Contrastive Learning-based Approachcited by 0