mlsanityData to 7 Oct 2026

FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference

arXiv 2410.20105 ↗First posted 26 Oct 2024cs.LG cs.CRcited by 16

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Abstract (arXiv)

Personalized Federated Graph Learning (pFGL) facilitates the decentralized training of Graph Neural Networks (GNNs) without compromising privacy while accommodating personalized requirements for non-IID participants. In cross-domain scenarios, structural heterogeneity poses significant challenges for pFGL. Nevertheless, previous pFGL methods incorrectly share non-generic knowledge globally and fail to tailor personalized solutions locally under domain structural shift. We innovatively reveal that the spectral nature of graphs can well reflect inherent domain structural shifts. Correspondingly, our method overcomes it by sharing generic spectral knowledge. Moreover, we indicate the biased message-passing schemes for graph structures and propose the personalized preference module. Combining both strategies, we propose our pFGL framework FedSSP which Shares generic Spectral knowledge while satisfying graph Preferences. Furthermore, We perform extensive experiments on cross-dataset and cross-domain settings to demonstrate the superiority of our framework. The code is available at https://github.com/OakleyTan/FedSSP.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 43 of its 94 references that are in the map. First posted on arXiv 26 Oct 2024.

Builds on

43 of 94 references.

References · 43

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
  5. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  6. 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 718
  7. 2022Recipe for a General, Powerful, Scalable Graph Transformercited by 702
  8. 2017Federated Multi-Task Learningcited by 669
  9. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  10. 2021Rethinking Graph Transformers with Spectral Attentioncited by 468
  11. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
  12. 2021BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximationcited by 195
31 more
  1. 2022How Powerful are Spectral Graph Neural Networkscited by 169
  2. 2022NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphscited by 137
  3. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  4. 2024Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systemscited by 129
  5. 2021Federated Graph Classification over Non-IID Graphscited by 113
  6. 2024G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networkscited by 110
  7. 2021InfoGCL: Information-Aware Graph Contrastive Learningcited by 105
  8. 2023Specformer: Spectral Graph Neural Networks Meet Transformerscited by 102
  9. 2020Building powerful and equivariant graph neural networks with structural message-passingcited by 95
  10. 2022How Powerful are K-hop Message Passing Graph Neural Networkscited by 90
  11. 2022Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisitedcited by 87
  12. 2019LanczosNet: Multi-Scale Deep Graph Convolutional Networkscited by 73
  13. 2022Does GNN Pretraining Help Molecular Representation?cited by 59
  14. 2022Personalized Subgraph Federated Learningcited by 54
  15. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  16. 2023Does Graph Distillation See Like Vision Dataset Counterpart?cited by 45
  17. 2023Eliminating Domain Bias for Federated Learning in Representation Spacecited by 38
  18. 2021Federated Graph Learning -- A Position Papercited by 34
  19. 2023A Survey on Spectral Graph Neural Networkscited by 26
  20. 2024TILP: Differentiable Learning of Temporal Logical Rules on Knowledge Graphscited by 21
  21. 2024Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmarkcited by 18
  22. 2020Self-Supervised Graph Transformer on Large-Scale Molecular Datacited by 17
  23. 2023Graph Distillation with Eigenbasis Matchingcited by 16
  24. 2021On Bridging Generic and Personalized Federated Learning for Image Classificationcited by 15
  25. 2022Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applicationscited by 14
  26. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  27. 2024Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graphcited by 10
  28. 2024Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformerscited by 9
  29. 2021FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networkscited by 5
  30. 2024EpiLearn: A Python Library for Machine Learning in Epidemic Modelingcited by 5
  31. 2024NTFormer: A Composite Node Tokenized Graph Transformer for Node Classificationcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 9; 2026: 6.

16 papers.

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  1. 2026MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learningcited by 11
  2. 2025FedBook: A Unified Federated Graph Foundation Codebook with Intra-domain and Inter-domain Knowledge Modelingcited by 8
  3. 2025S2FGL: Spatial Spectral Federated Graph Learningcited by 7
  4. 2025A Comprehensive Data-centric Overview of Federated Graph Learningcited by 7
  5. 2025Homophily Heterogeneity Matters in Graph Federated Learning: A Spectrum Sharing and Complementing Perspectivecited by 4
  6. 2025Curriculum Guided Personalized Subgraph Federated Learningcited by 4
  7. 2024Personalized One-shot Federated Graph Learning for Heterogeneous Clientscited by 3
  8. 2025Geometric Mixture Models for Electrolyte Conductivity Predictioncited by 2
  9. 2025Personalized Subgraph Federated Learning with Differentiable Auxiliary Projectionscited by 1
  10. 2026FedRio: Personalized Federated Social Bot Detection via Cooperative Reinforced Contrastive Adversarial Distillationcited by 1
  11. 2026Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneitycited by 1
  12. 2025Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learningcited by 0
4 more
  1. 2025Dissecting Federated-Graph Aggregation under Domain Shift: Importance-Aware Aggregation via Empirical Analysiscited by 0
  2. 2026Low-pass Personalized Subgraph Federated Recommendationcited by 0
  3. 2026FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learningcited by 0
  4. 2026Towards Adaptive Federated Graph Clustering: A Global Community-aware Contrastive Learning-based Approachcited by 0