mlsanityData to 7 Oct 2026

FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks

arXiv 2201.12433 ↗First posted 28 Jan 2022cs.LG cs.DCcited by 24

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Abstract (arXiv)

Methods for training models on graphs distributed across multiple clients have recently grown in popularity, due to the size of these graphs as well as regulations on keeping data where it is generated. However, the cross-client edges naturally exist among clients. Thus, distributed methods for training a model on a single graph incur either significant communication overhead between clients or a loss of available information to the training. We introduce the Federated Graph Convolutional Network (FedGCN) algorithm, which uses federated learning to train GCN models for semi-supervised node classification with fast convergence and little communication. Compared to prior methods that require extra communication among clients at each training round, FedGCN clients only communicate with the central server in one pre-training step, greatly reducing communication costs and allowing the use of homomorphic encryption to further enhance privacy. We theoretically analyze the tradeoff between FedGCN's convergence rate and communication cost under different data distributions. Experimental results show that our FedGCN algorithm achieves better model accuracy with 51.7% faster convergence on average and at least 100X less communication compared to prior work.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 30 of its 43 references that are in the map. First posted on arXiv 28 Jan 2022.

Builds on

30 of 43 references.

References · 30

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2017Inductive Representation Learning on Large Graphscited by 6,284
  5. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  6. 2019Simplifying Graph Convolutional Networkscited by 1,470
  7. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  8. 2018Federated Learning with Non-IID Datacited by 1,099
  9. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
  10. 2020Adaptive Federated Optimizationcited by 1,036
  11. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  12. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
18 more
  1. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  2. 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
  3. 2017Community Detection and Stochastic Block Modelscited by 317
  4. 2020SIGN: Scalable Inception Graph Neural Networkscited by 283
  5. 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 277
  6. 2021Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learningcited by 192
  7. 2020Scaling Graph Neural Networks with Approximate PageRankcited by 174
  8. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  9. 2013Consistency of spectral clustering in stochastic block modelscited by 128
  10. 2020Convergence and Stability of Graph Convolutional Networks on Large Random Graphscited by 75
  11. 2021Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modelingcited by 69
  12. 2020GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphscited by 64
  13. 2020ASFGNN: Automated Separated-Federated Graph Neural Networkcited by 36
  14. 2019Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Predictioncited by 30
  15. 2021SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networkscited by 30
  16. 2021Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networkscited by 24
  17. 2018Analysis of spectral clustering algorithms for community detection: the general bipartite settingcited by 20
  18. 2020Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Modelcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 0; 2024: 7; 2025: 12; 2026: 3.

24 papers.

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  1. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  2. 2024A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challengescited by 54
  3. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  4. 2025Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglementcited by 14
  5. 2025A Comprehensive Data-centric Overview of Federated Graph Learningcited by 7
  6. 2025Subgraph Federated Learning for Local Generalizationcited by 6
  7. 2025Subgraph Federated Learning via Spectral Methodscited by 6
  8. 2024Decoupled Subgraph Federated Learningcited by 3
  9. 2024FedGraph: A Research Library and Benchmark for Federated Graph Learningcited by 3
  10. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2
  11. 2025FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysiscited by 2
  12. 2024FedGIG: Graph Inversion from Gradient in Federated Learningcited by 1
12 more
  1. 2025Deal: Distributed End-to-End GNN Inference for All Nodescited by 1
  2. 2025Federated Prototype Graph Learningcited by 1
  3. 2025Personalized Subgraph Federated Learning with Differentiable Auxiliary Projectionscited by 1
  4. 2024Federated Hypergraph Learning with Local Differential Privacy: Toward Privacy-Aware Hypergraph Structure Completioncited by 0
  5. 2024Enhancing Federated Graph Learning via Adaptive Fusion of Structural and Node Characteristicscited by 0
  6. 2025Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID Graphscited by 0
  7. 2025Optimizing Federated Learning using Remote Embeddings for Graph Neural Networkscited by 0
  8. 2025PassREfinder-FL: Privacy-Preserving Credential Stuffing Risk Prediction via Graph-Based Federated Learning for Representing Password Reuse between Websitescited by 0
  9. 2025FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphscited by 0
  10. 2026Federated Cross-Client Subgraph Pattern Detectioncited by 0
  11. 2026Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networkscited by 0
  12. 2026DG-CoLearn: An Efficient Collaborative Learning Framework for Dynamic Graphscited by 0