mlsanityData to 14 Oct 2026

Decoupled Subgraph Federated Learning

arXiv 2402.19163 ↗First posted 29 Feb 2024cs.LG cs.IT math.ITcited by 3

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Abstract (arXiv)

We address the challenge of federated learning on graph-structured data distributed across multiple clients. Specifically, we focus on the prevalent scenario of interconnected subgraphs, where interconnections between different clients play a critical role. We present a novel framework for this scenario, named FedStruct, that harnesses deep structural dependencies. To uphold privacy, unlike existing methods, FedStruct eliminates the necessity of sharing or generating sensitive node features or embeddings among clients. Instead, it leverages explicit global graph structure information to capture inter-node dependencies. We validate the effectiveness of FedStruct through experimental results conducted on six datasets for semi-supervised node classification, showcasing performance close to the centralized approach across various scenarios, including different data partitioning methods, varying levels of label availability, and number of clients.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 30 of its 39 references that are in the map. First posted on arXiv 29 Feb 2024.

Builds on

30 of 39 references.

References · 30

  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. 2018How Powerful are Graph Neural Networks?cited by 4,393
  6. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  7. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,102
  8. 2018Pitfalls of Graph Neural Network Evaluationcited by 1,007
  9. 2020Geom-GCN: Geometric Graph Convolutional Networkscited by 826
  10. 2019Graph Neural Networks for Social Recommendationcited by 714
  11. 2019MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixingcited by 443
  12. 2023A critical look at the evaluation of GNNs under heterophily: Are we really making progress?cited by 343
18 more
  1. 2020Improving Graph Neural Network Expressivity via Subgraph Isomorphism Countingcited by 313
  2. 2020Towards Deeper Graph Neural Networkscited by 296
  3. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 261
  4. 2017struc2vec: Learning Node Representations from Structural Identitycited by 215
  5. 2022Graph Neural Networks for Graphs with Heterophily: A Surveycited by 175
  6. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  7. 2022Federated Learning on Non-IID Graphs via Structural Knowledge Sharingcited by 88
  8. 2020Breaking the Communication-Privacy-Accuracy Trilemmacited by 68
  9. 2022Personalized Subgraph Federated Learningcited by 54
  10. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  11. 2020On the Equivalence of Decoupled Graph Convolution Network and Label Propagationcited by 49
  12. 2021On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphscited by 49
  13. 2021FedGraph: Federated Graph Learning with Intelligent Samplingcited by 34
  14. 2021FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Predictioncited by 26
  15. 2022FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networkscited by 24
  16. 2019How Robust Are Graph Neural Networks to Structural Noise?cited by 15
  17. 2024Deep Efficient Private Neighbor Generation for Subgraph Federated Learningcited by 14
  18. 2023Federated Learning over Coupled Graphscited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Subgraph Federated Learning via Spectral Methodscited by 6
  2. 2026Federated Layer-Wise Exchange for Cross-Client Money Laundering Pattern Detectioncited by 0
  3. 2026UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignmentcited by 0