mlsanityData to 14 Oct 2026

Personalized Subgraph Federated Learning

arXiv 2206.10206 ↗First posted 21 Jun 2022cs.LGcited by 54

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Abstract (arXiv)

Subgraphs of a larger global graph may be distributed across multiple devices, and only locally accessible due to privacy restrictions, although there may be links between subgraphs. Recently proposed subgraph Federated Learning (FL) methods deal with those missing links across local subgraphs while distributively training Graph Neural Networks (GNNs) on them. However, they have overlooked the inevitable heterogeneity between subgraphs comprising different communities of a global graph, consequently collapsing the incompatible knowledge from local GNN models. To this end, we introduce a new subgraph FL problem, personalized subgraph FL, which focuses on the joint improvement of the interrelated local GNNs rather than learning a single global model, and propose a novel framework, FEDerated Personalized sUBgraph learning (FED-PUB), to tackle it. Since the server cannot access the subgraph in each client, FED-PUB utilizes functional embeddings of the local GNNs using random graphs as inputs to compute similarities between them, and use the similarities to perform weighted averaging for server-side aggregation. Further, it learns a personalized sparse mask at each client to select and update only the subgraph-relevant subset of the aggregated parameters. We validate our FED-PUB for its subgraph FL performance on six datasets, considering both non-overlapping and overlapping subgraphs, on which it significantly outperforms relevant baselines. Our code is available at https://github.com/JinheonBaek/FED-PUB.

Key facts. Cited by 54 papers in the map (data through 14 Oct 2026). Builds on 45 of its 75 references that are in the map. First posted on arXiv 21 Jun 2022.

Builds on

45 of 75 references.

References · 45

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  4. 2017Graph Attention Networkscited by 8,923
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  9. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  10. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
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  12. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
33 more
  1. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  2. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  3. 2018Pitfalls of Graph Neural Network Evaluationcited by 1,007
  4. 2018Link Prediction Based on Graph Neural Networkscited by 832
  5. 2021Model-Contrastive Federated Learningcited by 766
  6. 2015Image-based Recommendations on Styles and Substitutescited by 706
  7. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 682
  8. 2020Federated Learning with Matched Averagingcited by 564
  9. 2019Federated Learning with Personalization Layerscited by 541
  10. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  11. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  12. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 459
  13. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 383
  14. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
  15. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 325
  16. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 325
  17. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  18. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  19. 2021Accurate Learning of Graph Representations with Graph Multiset Poolingcited by 120
  20. 2021Federated Graph Classification over Non-IID Graphscited by 114
  21. 2021FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendationcited by 98
  22. 2022DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Trainingcited by 74
  23. 2021FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learningcited by 69
  24. 2021Federated Social Recommendation with Graph Neural Networkcited by 56
  25. 2022FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learningcited by 43
  26. 2021Edge Representation Learning with Hypergraphscited by 37
  27. 2022Achieving Personalized Federated Learning with Sparse Local Modelscited by 31
  28. 2021SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networkscited by 30
  29. 2022Architecture Agnostic Federated Learning for Neural Networkscited by 30
  30. 2022Personalized Federated Learning With Graphcited by 29
  31. 2016Compressing Graphs and Indexes with Recursive Graph Bisectioncited by 8
  32. 2021Task-Adaptive Neural Network Search with Meta-Contrastive Learningcited by 8
  33. 2022Factorized-FL: Agnostic Personalized Federated Learning with Kernel Factorization & Similarity Matchingcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 4; 2024: 16; 2025: 16; 2026: 16.

54 papers.

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  1. 2022Federated Learning on Non-IID Graphs via Structural Knowledge Sharingcited by 88
  2. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  3. 2024Federated Graph Semantic and Structural Learningcited by 30
  4. 2024AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneitycited by 25
  5. 2025Ten Challenging Problems in Federated Foundation Modelscited by 20
  6. 2023A Survey for Federated Learning Evaluations: Goals and Measurescited by 16
  7. 2024FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preferencecited by 16
  8. 2024Deep Efficient Private Neighbor Generation for Subgraph Federated Learningcited by 14
  9. 2025Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglementcited by 14
  10. 2024Modeling Inter-Intra Heterogeneity for Graph Federated Learningcited by 12
  11. 2024Safety in Graph Machine Learning: Threats and Safeguardscited by 11
  12. 2022FedGS: Federated Graph-based Sampling with Arbitrary Client Availabilitycited by 10
38 more
  1. 2023FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networkscited by 7
  2. 2024Balancing Similarity and Complementarity for Federated Learningcited by 7
  3. 2025S2FGL: Spatial Spectral Federated Graph Learningcited by 7
  4. 2025A Comprehensive Data-centric Overview of Federated Graph Learningcited by 7
  5. 2025Subgraph Federated Learning for Local Generalizationcited by 6
  6. 2025Subgraph Federated Learning via Spectral Methodscited by 6
  7. 2023Explicit Personalization and Local Training: Double Communication Acceleration in Federated Learningcited by 5
  8. 2024FedGT: Federated Node Classification with Scalable Graph Transformercited by 5
  9. 2025Homophily Heterogeneity Matters in Graph Federated Learning: A Spectrum Sharing and Complementing Perspectivecited by 4
  10. 2024Decoupled Subgraph Federated Learningcited by 3
  11. 2024FedSheafHN: Personalized Federated Learning on Graph-structured Datacited by 3
  12. 2026STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learningcited by 3
  13. 2024FedRGL: Robust Federated Graph Learning for Label Noisecited by 2
  14. 2026Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approachcited by 2
  15. 2026FlatLand: Personalized Graph Federated Learning via Tailored Lorentz Spacecited by 2
  16. 2024FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reductioncited by 1
  17. 2024One Node Per User: Node-Level Federated Learning for Graph Neural Networkscited by 1
  18. 2025FedGrAINS: Personalized SubGraph Federated Learning with Adaptive Neighbor Samplingcited by 1
  19. 2025Federated Prototype Graph Learningcited by 1
  20. 2025Towards Unbiased Federated Graph Learning: Label and Topology Perspectivescited by 1
  21. 2025FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphscited by 1
  22. 2025Personalized Subgraph Federated Learning with Differentiable Auxiliary Projectionscited by 1
  23. 2025Personalized Subgraph Federated Learning with Sheaf Collaborationcited by 1
  24. 2026Heterogeneity-Aware Knowledge Sharing for Graph Federated Learningcited by 1
  25. 2026Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Frameworkcited by 1
  26. 2024DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learningcited by 0
  27. 2024Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Informationcited by 0
  28. 2024Attributed Graph Clustering in Collaborative Settingscited by 0
  29. 2025Optimizing Federated Learning using Remote Embeddings for Graph Neural Networkscited by 0
  30. 2025FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcastingcited by 0
  31. 2025FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphscited by 0
  32. 2026BoostFGL: Boosting Fairness in Federated Graph Learningcited by 0
  33. 2026DANCE: Dynamic, Available, Neighbor-gated Condensation for Federated Text-Attributed Graphscited by 0
  34. 2026Hide and Find: A Distributed Adversarial Attack on Federated Graph Learningcited by 0
  35. 2026Low-pass Personalized Subgraph Federated Recommendationcited by 0
  36. 2026Representation-Aligned Multi-Scale Personalization for Federated Learningcited by 0
  37. 2026Beyond Rigid Alignment: Graph Federated Learning via Dual Manifold Calibrationcited by 0
  38. 2026Generalized Category Discovery in Federated Graph Learningcited by 0

50 most cited of 54 All 54 (JSON)