mlsanityData to 7 Oct 2026

S2FGL: Spatial Spectral Federated Graph Learning

arXiv 2507.02409 ↗First posted 3 Jul 2025cs.LG cs.AIcited by 7

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Abstract (arXiv)

Federated Graph Learning (FGL) combines the privacy-preserving capabilities of federated learning (FL) with the strong graph modeling capability of Graph Neural Networks (GNNs). Current research addresses subgraph-FL from the structural perspective, neglecting the propagation of graph signals on spatial and spectral domains of the structure. From a spatial perspective, subgraph-FL introduces edge disconnections between clients, leading to disruptions in label signals and a degradation in the semantic knowledge of the global GNN. From a spectral perspective, spectral heterogeneity causes inconsistencies in signal frequencies across subgraphs, which makes local GNNs overfit the local signal propagation schemes. As a result, spectral client drift occurs, undermining global generalizability. To tackle the challenges, we propose a global knowledge repository to mitigate the challenge of poor semantic knowledge caused by label signal disruption. Furthermore, we design a frequency alignment to address spectral client drift. The combination of Spatial and Spectral strategies forms our framework S2FGL. Extensive experiments on multiple datasets demonstrate the superiority of S2FGL. The code is available at https://github.com/Wonder7racer/S2FGL.git.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 51 of its 74 references that are in the map. First posted on arXiv 3 Jul 2025.

Builds on

51 of 74 references.

References · 51

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  4. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  5. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  6. 2017Federated Multi-Task Learningcited by 669
  7. 2021Federated Learning Based on Dynamic Regularizationcited by 555
  8. 2021Rethinking Graph Transformers with Spectral Attentioncited by 468
  9. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 380
  10. 2023A critical look at the evaluation of GNNs under heterophily: Are we really making progress?cited by 332
  11. 2021BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximationcited by 195
  12. 2022Rethinking Graph Neural Networks for Anomaly Detectioncited by 185
38 more
  1. 2019SCAFFOLD: Stochastic Controlled Averaging for Federated Learningcited by 174
  2. 2022How Powerful are Spectral Graph Neural Networkscited by 169
  3. 2022Revisiting Heterophily For Graph Neural Networkscited by 169
  4. 2020A Semi-supervised Graph Attentive Network for Financial Fraud Detectioncited by 150
  5. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  6. 2021FairFed: Enabling Group Fairness in Federated Learningcited by 114
  7. 2021Federated Graph Classification over Non-IID Graphscited by 113
  8. 2022Federated Learning with Label Distribution Skew via Logits Calibrationcited by 108
  9. 2023Specformer: Spectral Graph Neural Networks Meet Transformerscited by 102
  10. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  11. 2021Asynchronous Federated Learning on Heterogeneous Devices: A Surveycited by 93
  12. 2022Federated Learning on Non-IID Graphs via Structural Knowledge Sharingcited by 88
  13. 2021Preservation of the Global Knowledge by Not-True Distillation in Federated Learningcited by 87
  14. 2022Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisitedcited by 87
  15. 2022Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Featurescited by 63
  16. 2022FedCorr: Multi-Stage Federated Learning for Label Noise Correctioncited by 61
  17. 2022Personalized Subgraph Federated Learningcited by 54
  18. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  19. 2020FedE: Embedding Knowledge Graphs in Federated Settingcited by 46
  20. 2024FedGTA: Topology-aware Averaging for Federated Graph Learningcited by 44
  21. 2022Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrumcited by 43
  22. 2023Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learningcited by 39
  23. 2023Eliminating Domain Bias for Federated Learning in Representation Spacecited by 38
  24. 2021Federated Graph Learning -- A Position Papercited by 34
  25. 2024A Review of Graph Neural Networks in Epidemic Modelingcited by 31
  26. 2022Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guaranteescited by 30
  27. 2024Federated Graph Semantic and Structural Learningcited by 30
  28. 2024Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversitycited by 28
  29. 2023TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Trainingcited by 27
  30. 2024FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learningcited by 27
  31. 2023Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaborationcited by 23
  32. 2022Fairness in Federated Learning via Core-Stabilitycited by 22
  33. 2023FedFA: Federated Feature Augmentationcited by 22
  34. 2023GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learningcited by 18
  35. 2024FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preferencecited by 16
  36. 2023Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombinationcited by 13
  37. 2021Federated Robustness Propagation: Sharing Robustness in Heterogeneous Federated Learningcited by 9
  38. 2024Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detectioncited by 8

50 most cited of 51 All 51 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 7.

7 papers.

  1. 2026MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learningcited by 11
  2. 2026STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learningcited by 3
  3. 2026PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learningcited by 3
  4. 2026Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneitycited by 1
  5. 2026Low-pass Personalized Subgraph Federated Recommendationcited by 0
  6. 2026Towards Federated Long-Tailed Graph Learning: An Energy-Guided Dual Decoupling Approachcited by 0
  7. 2026Towards Adaptive Federated Graph Clustering: A Global Community-aware Contrastive Learning-based Approachcited by 0