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<p>Deep-learning-based human activity recognition (HAR) has been widely studied and applied in recent years, but it raises privacy concerns. Federated learning (FL) enables collaborative training without sharing raw data, thereby protecting user privacy. However, FL for HAR is challenged by three coupled factors: non-IID data across clients, aggregation under heterogeneous local models, and stringent computation and bandwidth budgets on edge hardware. To address these factors, this work introduces FedSynHAR, a lightweight FL framework that combines Gradient-Importance-based Adaptive Pruning (GIAP) with Channel-guided Feature-level Mutual Distillation (CFMD). GIAP prunes both server and client networks based on gradient importance, reducing computational and communication overhead; CFMD uses channel importance to guide mutual distillation between local and proxy models and mitigates pruning-induced degradation to improve robustness under non-IID conditions. Experiments on UCI-HAR and PAMAP2 demonstrate the effectiveness of FedSynHAR for federated HAR. On UCI-HAR, FedSynHAR converges about 2<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\times\)</EquationSource> </InlineEquation> faster than FedAvg, achieves 94.91% accuracy under non-IID settings, and reduces overhead by up to two orders of magnitude. Results on PAMAP2 further support the robustness of FedSynHAR under stronger heterogeneity.</p>

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FedSynHAR: a framework based on feature-enhanced adaptive pruning-mutual distillation for federated human activity recognition

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月17日

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  • Cheng Wang,
  • Rongze Fan

摘要

Deep-learning-based human activity recognition (HAR) has been widely studied and applied in recent years, but it raises privacy concerns. Federated learning (FL) enables collaborative training without sharing raw data, thereby protecting user privacy. However, FL for HAR is challenged by three coupled factors: non-IID data across clients, aggregation under heterogeneous local models, and stringent computation and bandwidth budgets on edge hardware. To address these factors, this work introduces FedSynHAR, a lightweight FL framework that combines Gradient-Importance-based Adaptive Pruning (GIAP) with Channel-guided Feature-level Mutual Distillation (CFMD). GIAP prunes both server and client networks based on gradient importance, reducing computational and communication overhead; CFMD uses channel importance to guide mutual distillation between local and proxy models and mitigates pruning-induced degradation to improve robustness under non-IID conditions. Experiments on UCI-HAR and PAMAP2 demonstrate the effectiveness of FedSynHAR for federated HAR. On UCI-HAR, FedSynHAR converges about 2 \(\times\) faster than FedAvg, achieves 94.91% accuracy under non-IID settings, and reduces overhead by up to two orders of magnitude. Results on PAMAP2 further support the robustness of FedSynHAR under stronger heterogeneity.