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<p>Federated Learning (FL) has emerged as a decentralized alternative for Human Activity Recognition (HAR), allowing models to be trained across devices without compromising user privacy. However, existing FL frameworks still require a large amount of labeled data to achieve effective performance, particularly when facing significant data heterogeneity challenges in natural environments. To address this, we propose Federated Active User Participation (<i>FAUP</i>), an adaptive FL framework that reduces data requirements while maintaining a low communication overhead. FAUP employs adaptive clustering to prioritize “Active Users” with higher signal variance, enabling model aggregation based on similar activity patterns. By leveraging these informative data samples, FAUP accelerates convergence while maintaining high accuracy. We evaluated FAUP on four publicly available HAR datasets involving 148 users. The results demonstrate that FAUP achieves comparable accuracy to existing frameworks while requiring only 50%–80% of the training data in controlled settings, and less than 10% of the data in natural, real-world settings.</p>

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Federated Active User Participation (FAUP)

  • 期刊论文

  • 发表日期：

    2026年6月30日

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  • Atia Akram,
  • Asma Ahmad Farhan,
  • Amna Basharat

摘要

Federated Learning (FL) has emerged as a decentralized alternative for Human Activity Recognition (HAR), allowing models to be trained across devices without compromising user privacy. However, existing FL frameworks still require a large amount of labeled data to achieve effective performance, particularly when facing significant data heterogeneity challenges in natural environments. To address this, we propose Federated Active User Participation (FAUP), an adaptive FL framework that reduces data requirements while maintaining a low communication overhead. FAUP employs adaptive clustering to prioritize “Active Users” with higher signal variance, enabling model aggregation based on similar activity patterns. By leveraging these informative data samples, FAUP accelerates convergence while maintaining high accuracy. We evaluated FAUP on four publicly available HAR datasets involving 148 users. The results demonstrate that FAUP achieves comparable accuracy to existing frameworks while requiring only 50%–80% of the training data in controlled settings, and less than 10% of the data in natural, real-world settings.