用多频联邦学习提升头戴设备的隐私保护动作识别
Multi-Frequency Federated Learning for Human Activity Recognition Using Head-Worn Sensors
- 设计多频联邦学习框架，跨不同采样频率设备协同训练
- 在两个数据集上优于单一频率方法，准确率显著提升
- 适合关注隐私保护与异构设备协作的智能健康研究者
人体动作识别（HAR）在健康与老年照护等领域具有重要应用。传统HAR依赖集中式用户数据构建模型，存在隐私泄露风险。本文提出多频联邦学习（Multi-Frequency Federated Learning, MF-FL），实现：(1) 隐私感知的机器学习；(2) 在采样频率各异的设备间联合学习模型。研究聚焦头戴设备（如耳塞和智能眼镜），相较传统的智能手表或手机类HAR，该领域仍较未被充分探索。实验结果表明，在两个数据集上，该方法优于针对特定频率设计的基准方法，展现出多频联邦学习在HAR任务中的广阔前景。所提网络架构已开源，可供后续研究与开发使用。
原文摘要 · Abstract (English)
Human Activity Recognition (HAR) benefits various application domains, including health and elderly care. Traditional HAR involves constructing pipelines reliant on centralized user data, which can pose privacy concerns as they necessitate the uploading of user data to a centralized server. This work proposes multi-frequency Federated Learning (FL) to enable: (1) privacy-aware ML; (2) joint ML model learning across devices with varying sampling frequency. We focus on head-worn devices (e.g., earbuds and smart glasses), a relatively unexplored domain compared to traditional smartwatch- or smartphone-based HAR. Results have shown improvements on two datasets against frequency-specific approaches, indicating a promising future in the multi-frequency FL-HAR task. The proposed network's implementation is publicly available for further research and development.
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