mlsanityData to 14 Oct 2026

STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks

arXiv 2111.06750 ↗First posted 12 Nov 2021cs.LG cs.AIcited by 14

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Abstract (arXiv)

We present a spatial-temporal federated learning framework for graph neural networks, namely STFL. The framework explores the underlying correlation of the input spatial-temporal data and transform it to both node features and adjacency matrix. The federated learning setting in the framework ensures data privacy while achieving a good model generalization. Experiments results on the sleep stage dataset, ISRUC_S3, illustrate the effectiveness of STFL on graph prediction tasks.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 15 of its 28 references that are in the map. First posted on arXiv 12 Nov 2021.

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15 of 28 references.

References · 15

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2017Graph Attention Networkscited by 8,923
  4. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  5. 2017Inductive Representation Learning on Large Graphscited by 6,320
  6. 2019Advances and Open Problems in Federated Learningcited by 3,046
  7. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
  8. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,209
  9. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  10. 2016Structured Sequence Modeling with Graph Convolutional Recurrent Networkscited by 275
  11. 2015Structural-RNN: Deep Learning on Spatio-Temporal Graphscited by 234
  12. 2018Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecastingcited by 146
3 more
  1. 2021Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modelingcited by 69
  2. 2021Multi-View Spatial-Temporal Graph Convolutional Networks with Domain Generalization for Sleep Stage Classificationcited by 37
  3. 2021Federated Graph Learning -- A Position Papercited by 35

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Cited by year

Citing papers per year: 2021: 1; 2022: 5; 2023: 1; 2024: 3; 2025: 4.

14 papers.

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  1. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 89
  2. 2022Federated Learning on Non-IID Graphs via Structural Knowledge Sharingcited by 88
  3. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  4. 2023Spatio-Temporal Graph Neural Networks: A Surveycited by 23
  5. 2022Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applicationscited by 14
  6. 2024Safety in Graph Machine Learning: Threats and Safeguardscited by 11
  7. 2022An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learningcited by 7
  8. 2025A Comprehensive Data-centric Overview of Federated Graph Learningcited by 7
  9. 2021GPS: A Policy-driven Sampling Approach for Graph Representation Learningcited by 5
  10. 2024Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagationcited by 5
  11. 2025GRAIN: Exact Graph Reconstruction from Gradientscited by 5
  12. 2024FedGraph: A Research Library and Benchmark for Federated Graph Learningcited by 3
2 more
  1. 2025FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphscited by 1
  2. 2025MetaSTH-Sleep: Towards Effective Few-Shot Sleep Stage Classification for Health Management with Spatial-Temporal Hypergraph Enhanced Meta-Learningcited by 0