mlsanityData to 7 Oct 2026

OpenFGL: A Comprehensive Benchmark for Federated Graph Learning

arXiv 2408.16288 ↗First posted 29 Aug 2024cs.LG cs.AI cs.DBcited by 16

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Abstract (arXiv)

Federated graph learning (FGL) is a promising distributed training paradigm for graph neural networks across multiple local systems without direct data sharing. This approach inherently involves large-scale distributed graph processing, which closely aligns with the challenges and research focuses of graph-based data systems. Despite the proliferation of FGL, the diverse motivations from real-world applications, spanning various research backgrounds and settings, pose a significant challenge to fair evaluation. To fill this gap, we propose OpenFGL, a unified benchmark designed for the primary FGL scenarios: Graph-FL and Subgraph-FL. Specifically, OpenFGL includes 42 graph datasets from 18 application domains, 8 federated data simulation strategies that emphasize different graph properties, and 5 graph-based downstream tasks. Additionally, it offers 18 recently proposed SOTA FGL algorithms through a user-friendly API, enabling a thorough comparison and comprehensive evaluation of their effectiveness, robustness, and efficiency. Our empirical results demonstrate the capabilities of FGL while also highlighting its potential limitations, providing valuable insights for future research in this growing field, particularly in fostering greater interdisciplinary collaboration between FGL and data systems.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 55 of its 138 references that are in the map. First posted on arXiv 29 Aug 2024.

Builds on

55 of 138 references.

References · 55

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  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. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  6. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
  7. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  8. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  9. 2019Simplifying Graph Convolutional Networkscited by 1,470
  10. 2019Deep Leakage from Gradientscited by 1,004
  11. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
  12. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
38 more
  1. 2020Simple and Deep Graph Convolutional Networkscited by 857
  2. 2020Geom-GCN: Geometric Graph Convolutional Networkscited by 821
  3. 2021Model-Contrastive Federated Learningcited by 762
  4. 2019Heterogeneous Graph Attention Networkcited by 732
  5. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  6. 2020Heterogeneous Graph Transformercited by 581
  7. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  8. 2021Datasets: A Community Library for Natural Language Processingcited by 526
  9. 2019Graph U-Netscited by 489
  10. 2016Geometric deep learning on graphs and manifolds using mixture model CNNscited by 468
  11. 2019Self-Attention Graph Poolingcited by 421
  12. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  13. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  14. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 309
  15. 2020MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embeddingcited by 304
  16. 2018Learning Word Vectors for 157 Languagescited by 279
  17. 2019Adversarial Attacks on Graph Neural Networks via Meta Learningcited by 261
  18. 2021Is Homophily a Necessity for Graph Neural Networks?cited by 182
  19. 2022FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correctioncited by 181
  20. 2021Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networkscited by 176
  21. 2022Graph Neural Networks for Graphs with Heterophily: A Surveycited by 175
  22. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  23. 2021Federated Graph Classification over Non-IID Graphscited by 113
  24. 2020Feature Inference Attack on Model Predictions in Vertical Federated Learningcited by 105
  25. 2017PCPNET: Learning Local Shape Properties from Raw Point Cloudscited by 97
  26. 2022Federated Learning on Non-IID Graphs via Structural Knowledge Sharingcited by 88
  27. 2020Scalable Graph Neural Networks via Bidirectional Propagationcited by 87
  28. 2022Graph Attention Multi-Layer Perceptroncited by 82
  29. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
  30. 2024FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learningcited by 47
  31. 2020Federated Model Distillation with Noise-Free Differential Privacycited by 40
  32. 2020Path Integral Based Convolution and Pooling for Graph Neural Networkscited by 35
  33. 2021FedGL: Federated Graph Learning Framework with Global Self-Supervisioncited by 34
  34. 2022SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compressioncited by 34
  35. 2021Fast-adapting and Privacy-preserving Federated Recommender Systemcited by 30
  36. 2024Federated Graph Semantic and Structural Learningcited by 30
  37. 2023Convergence Analysis of Sequential Federated Learning on Heterogeneous Datacited by 23
  38. 2023HeteFedRec: Federated Recommender Systems with Model Heterogeneitycited by 20

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Citing papers per year: 2025: 3; 2026: 13.

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  1. 2026MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learningcited by 11
  2. 2026PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learningcited by 3
  3. 2025Towards Unbiased Federated Graph Learning: Label and Topology Perspectivescited by 1
  4. 2026Causality-inspired Federated Learning for Dynamic Spatio-Temporal Graphscited by 1
  5. 2026Towards Robust Federated Multimodal Graph Learning under Modality Heterogeneitycited by 1
  6. 2026FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learningcited by 1
  7. 2026Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneitycited by 1
  8. 2025FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcastingcited by 0
  9. 2025Dissecting Federated-Graph Aggregation under Domain Shift: Importance-Aware Aggregation via Empirical Analysiscited by 0
  10. 2026On the Geometric Coherence of Global Aggregation in Federated Graph Neural Networkscited by 0
  11. 2026Generalized Category Discovery in Federated Graph Learningcited by 0
  12. 2026Towards Federated Long-Tailed Graph Learning: An Energy-Guided Dual Decoupling Approachcited by 0
4 more
  1. 2026FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learningcited by 0
  2. 2026Privacy-Preserving Topology-Guided Safety for LLM-Based Multi-Agent Systems via Federated Graph Learningcited by 0
  3. 2026Cost Characterization of Vertically Partitioned Federated Knowledge Graphscited by 0
  4. 2026Structural Negative Transfer in Federated Graph Neural Networks: Diagnosis, Causal Investigation, and the Limits of Divergence-Aware Mitigationcited by 0