基于差分隐私的联邦拆分学习提升人体活动识别精度与效率
Federated Split Learning for Human Activity Recognition with Differential Privacy
- 采用联邦拆分学习融合加速度计与陀螺仪数据，提升模型精度
- 相比传统联邦学习，准确率更高、训练损失更低、通信更快
- 在保护隐私的前提下实现高精度识别，适合边缘设备应用
本文提出一种新型智能人体活动识别（HAR）框架——基于差分隐私（DP）的联邦拆分学习（FSL），适用于边缘网络。该框架融合加速度计与陀螺仪数据，在真实数据集上显著提升识别准确率。对比实验表明，相较于传统联邦学习（FL），FSL在准确率和损失指标上均表现更优，且每轮训练通信时间更短，效率更高。同时，研究分析了不同数据设置下差分隐私机制的隐私-性能权衡，揭示了隐私保障与模型精度之间的平衡关系。本工作为边缘侧人体活动识别提供了高效且安全的新范式。
原文摘要 · Abstract (English)
This paper proposes a novel intelligent human activity recognition (HAR) framework based on a new design of Federated Split Learning (FSL) with Differential Privacy (DP) over edge networks. Our FSL-DP framework leverages both accelerometer and gyroscope data, achieving significant improvements in HAR accuracy. The evaluation includes a detailed comparison between traditional Federated Learning (FL) and our FSL framework, showing that the FSL framework outperforms FL models in both accuracy and loss metrics. Additionally, we examine the privacy-performance trade-off under different data settings in the DP mechanism, highlighting the balance between privacy guarantees and model accuracy. The results also indicate that our FSL framework achieves faster communication times per training round compared to traditional FL, further emphasizing its efficiency and effectiveness. This work provides valuable insight and a novel framework which was tested on a real-life dataset.
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