mlsanityData to 14 Oct 2026

Heterogeneity-Aware Knowledge Sharing for Graph Federated Learning

arXiv 2601.21589 ↗First posted 29 Jan 2026cs.LGcited by 1

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Abstract (arXiv)

Graph Federated Learning (GFL) enables distributed graph representation learning while protecting the privacy of graph data. However, GFL suffers from heterogeneity arising from diverse node features and structural topologies across multiple clients. To address both types of heterogeneity, we propose a novel graph Federated learning method via Semantic and Structural Alignment (FedSSA), which shares the knowledge of both node features and structural topologies. For node feature heterogeneity, we propose a novel variational model to infer class-wise node distributions, so that we can cluster clients based on inferred distributions and construct cluster-level representative distributions. We then minimize the divergence between local and cluster-level distributions to facilitate semantic knowledge sharing. For structural heterogeneity, we employ spectral Graph Neural Networks (GNNs) and propose a spectral energy measure to characterize structural information, so that we can cluster clients based on spectral energy and build cluster-level spectral GNNs. We then align the spectral characteristics of local spectral GNNs with those of cluster-level spectral GNNs to enable structural knowledge sharing. Experiments on six homophilic and five heterophilic graph datasets under both non-overlapping and overlapping partitioning settings demonstrate that FedSSA consistently outperforms eleven state-of-the-art methods.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 31 of its 45 references that are in the map. First posted on arXiv 29 Jan 2026.

Builds on

31 of 45 references.

References · 31

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  5. 2016Variational Graph Auto-Encoderscited by 1,382
  6. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  7. 2019Federated Learning with Personalization Layerscited by 541
  8. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  9. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  10. 2014Semi-Supervised Learning with Deep Generative Modelscited by 496
  11. 2023A critical look at the evaluation of GNNs under heterophily: Are we really making progress?cited by 343
  12. 2021BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximationcited by 195
19 more
  1. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  2. 2021Federated Graph Classification over Non-IID Graphscited by 114
  3. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 99
  4. 2022Graph Attention Multi-Layer Perceptroncited by 83
  5. 2022Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyondcited by 60
  6. 2022Personalized Subgraph Federated Learningcited by 54
  7. 2024FedGTA: Topology-aware Averaging for Federated Graph Learningcited by 44
  8. 2023Graph Neural Networks with Learnable and Optimal Polynomial Basescited by 36
  9. 2024FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learningcited by 27
  10. 2024AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneitycited by 25
  11. 2024How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashingcited by 24
  12. 2022FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregationcited by 21
  13. 2024Modeling Inter-Intra Heterogeneity for Graph Federated Learningcited by 12
  14. 2022Hyperspectral Image Classification With Contrastive Graph Convolutional Networkcited by 8
  15. 2023FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimizationcited by 7
  16. 2024Vertical Federated Learning with Missing Features During Training and Inferencecited by 6
  17. 2024A Survey of Distributed Graph Algorithms on Massive Graphscited by 5
  18. 2023Atom-Motif Contrastive Transformer for Molecular Property Predictioncited by 4
  19. 2025Homophily Heterogeneity Matters in Graph Federated Learning: A Spectrum Sharing and Complementing Perspectivecited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Beyond Rigid Alignment: Graph Federated Learning via Dual Manifold Calibrationcited by 0