mlsanityData to 7 Oct 2026

Federated Learning over Coupled Graphs

arXiv 2301.11099 ↗First posted 26 Jan 2023cs.LGcited by 12

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Abstract (arXiv)

Graphs are widely used to represent the relations among entities. When one owns the complete data, an entire graph can be easily built, therefore performing analysis on the graph is straightforward. However, in many scenarios, it is impractical to centralize the data due to data privacy concerns. An organization or party only keeps a part of the whole graph data, i.e., graph data is isolated from different parties. Recently, Federated Learning (FL) has been proposed to solve the data isolation issue, mainly for Euclidean data. It is still a challenge to apply FL on graph data because graphs contain topological information which is notorious for its non-IID nature and is hard to partition. In this work, we propose a novel FL framework for graph data, FedCog, to efficiently handle coupled graphs that are a kind of distributed graph data, but widely exist in a variety of real-world applications such as mobile carriers' communication networks and banks' transaction networks. We theoretically prove the correctness and security of FedCog. Experimental results demonstrate that our method FedCog significantly outperforms traditional FL methods on graphs. Remarkably, our FedCog improves the accuracy of node classification tasks by up to 14.7%.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 32 of its 54 references that are in the map. First posted on arXiv 26 Jan 2023.

Builds on

32 of 54 references.

References · 32

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2019Simplifying Graph Convolutional Networkscited by 1,470
  5. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  6. 2020Adaptive Federated Optimizationcited by 1,036
  7. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
  8. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  9. 2019Heterogeneous Graph Attention Networkcited by 732
  10. 2015Image-based Recommendations on Styles and Substitutescited by 702
  11. 2020Adaptive Universal Generalized PageRank Graph Neural Networkcited by 592
  12. 2021Federated Learning Based on Dynamic Regularizationcited by 555
20 more
  1. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 349
  2. 2020FetchSGD: Communication-Efficient Federated Learning with Sketchingcited by 178
  3. 2022Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learningcited by 172
  4. 2017Encoding Sentences with Graph Convolutional Networks for Semantic Role Labelingcited by 137
  5. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  6. 2020Federated Visual Classification with Real-World Data Distributioncited by 126
  7. 2020Scalable Graph Neural Networks via Bidirectional Propagationcited by 87
  8. 2021Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modelingcited by 69
  9. 2021Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacycited by 66
  10. 2020GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphscited by 64
  11. 2020Vertically Federated Graph Neural Network for Privacy-Preserving Node Classificationcited by 61
  12. 2020FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learningcited by 60
  13. 2022Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillationcited by 45
  14. 2021FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learningcited by 43
  15. 2021FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learningcited by 39
  16. 2020ASFGNN: Automated Separated-Federated Graph Neural Networkcited by 36
  17. 2019Optimizing Generalized PageRank Methods for Seed-Expansion Community Detectioncited by 35
  18. 2021FedGL: Federated Graph Learning Framework with Global Self-Supervisioncited by 34
  19. 2020Communication-Efficient Federated Learning with Compensated Overlap-FedAvgcited by 31
  20. 2021DGCN: Diversified Recommendation with Graph Convolutional Networkscited by 29

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 3; 2026: 2.

12 papers.

  1. 2024FedGTA: Topology-aware Averaging for Federated Graph Learningcited by 44
  2. 2024Safety in Graph Machine Learning: Threats and Safeguardscited by 11
  3. 2023Privacy-Preserving Graph Machine Learning from Data to Computation: A Surveycited by 10
  4. 2025A Comprehensive Data-centric Overview of Federated Graph Learningcited by 7
  5. 2025Subgraph Federated Learning via Spectral Methodscited by 6
  6. 2024HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learningcited by 4
  7. 2024Decoupled Subgraph Federated Learningcited by 3
  8. 2025FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphscited by 1
  9. 2024Federated Hypergraph Learning with Local Differential Privacy: Toward Privacy-Aware Hypergraph Structure Completioncited by 0
  10. 2024Federated Temporal Graph Clusteringcited by 0
  11. 2026Low-pass Personalized Subgraph Federated Recommendationcited by 0
  12. 2026Federated Cross-Client Subgraph Pattern Detectioncited by 0