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Clustered Federated Spatio-Temporal Graph Attention Networks for Skeleton-Based Action Recognition.

Tao Yu1, Sandro Pinto1, Tiago Gomes1

  • 1Centro Algoritmi, University do Minho, 4800-058 Guimarães, Portugal.

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|December 11, 2025
PubMed
Summary

Clustered Federated Spatio-Temporal Graph Attention Networks (CF-STGAT) improve skeleton-based action recognition under client heterogeneity. This novel framework enhances model convergence and accuracy by dynamically grouping clients using attention mechanisms.

Keywords:
clustered federated learninginter-cluster regularizationskeleton-based action recognitionspatio-temporal graph attention

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Federated learning (FL) for skeleton-based action recognition is challenging due to client heterogeneity, leading to model drift and convergence issues with standard methods like FedAvg.
  • Existing FL approaches struggle to maintain stable performance when clients exhibit significant variations in their data distributions and model parameters.

Purpose of the Study:

  • To introduce a novel clustered FL framework, Clustered Federated Spatio-Temporal Graph Attention Networks (CF-STGAT), designed to address client heterogeneity in skeleton-based action recognition.
  • To enhance the stability and accuracy of FL models by dynamically grouping clients and performing attention-weighted inter-cluster fusion.

Main Methods:

  • Leveraged attention-derived spatio-temporal statistics from local Spatio-Temporal Graph Attention Network (STGAT) models to dynamically group clients.
  • Implemented a server-side process involving extraction, normalization, and PCA projection of multi-head parameter-based attention descriptors for K-means clustering.
  • Utilized attention-similarity weighting to compute a global reference for regularizing cluster models via a lightweight fusion step, keeping local training unchanged.

Main Results:

  • CF-STGAT consistently outperformed strong FL baselines on the NTU RGB+D 60/120 datasets, achieving significant absolute top-1 accuracy gains over FedAvg.
  • Demonstrated improved performance across different evaluation settings (X-Sub/X-Setup), with notable gains of +0.84/+4.09 on NTU 60 and +7.98/+4.18 on NTU 120.
  • Observed smoother per-client training trajectories and lower terminal test loss, indicating enhanced model stability and convergence.

Conclusions:

  • Attention-guided clustering and inter-cluster fusion are effective complementary strategies for mitigating within-group variance and cross-cluster divergence in FL.
  • The CF-STGAT framework offers a robust solution for skeleton-based action recognition under strong client heterogeneity without altering local training procedures.
  • The proposed method effectively enhances federated learning performance by leveraging attention mechanisms for dynamic client grouping and model alignment.