mlsanityData to 7 Oct 2026

FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search

arXiv 2104.04141 ↗First posted 9 Apr 2021cs.LG cs.DCcited by 8

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Abstract (arXiv)

Recently, some Neural Architecture Search (NAS) techniques are proposed for the automatic design of Graph Convolutional Network (GCN) architectures. They bring great convenience to the use of GCN, but could hardly apply to the Federated Learning (FL) scenarios with distributed and private datasets, which limit their applications. Moreover, they need to train many candidate GCN models from scratch, which is inefficient for FL. To address these challenges, we propose FL-AGCNS, an efficient GCN NAS algorithm suitable for FL scenarios. FL-AGCNS designs a federated evolutionary optimization strategy to enable distributed agents to cooperatively design powerful GCN models while keeping personal information on local devices. Besides, it applies the GCN SuperNet and a weight sharing strategy to speed up the evaluation of GCN models. Experimental results show that FL-AGCNS can find better GCN models in short time under the FL framework, surpassing the state-of-the-arts NAS methods and GCN models.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 21 of its 32 references that are in the map. First posted on arXiv 9 Apr 2021.

Builds on

21 of 32 references.

References · 21

  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. 2018How Powerful are Graph Neural Networks?cited by 4,355
  6. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  7. 2018DARTS: Differentiable Architecture Searchcited by 1,782
  8. 2019Simplifying Graph Convolutional Networkscited by 1,470
  9. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  10. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
  11. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  12. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
9 more
  1. 2015Gated Graph Sequence Neural Networkscited by 792
  2. 2018Session-based Recommendation with Graph Neural Networkscited by 312
  3. 2019Searching for A Robust Neural Architecture in Four GPU Hourscited by 258
  4. 2019Graph Neural Networks with convolutional ARMA filterscited by 185
  5. 2020FedVision: An Online Visual Object Detection Platform Powered by Federated Learningcited by 121
  6. 2018Attention-based Graph Neural Network for Semi-supervised Learningcited by 100
  7. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  8. 2019CARS: Continuous Evolution for Efficient Neural Architecture Searchcited by 79
  9. 2020Simplifying Architecture Search for Graph Neural Networkcited by 27

Built on by

Cited by year

Citing papers per year: 2021: 4; 2022: 4.

8 papers.

  1. 2021Federated Graph Classification over Non-IID Graphscited by 113
  2. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
  3. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  4. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 38
  5. 2021Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Settingcited by 19
  6. 2022Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applicationscited by 14
  7. 2022Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directionscited by 8
  8. 2022Gromov-Wasserstein Discrepancy with Local Differential Privacy for Distributed Structural Graphscited by 6