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.

翻译：本文提出了一种新颖的智能人类活动识别框架，该框架基于在边缘网络上结合差分隐私的联邦分割学习新设计。我们的FSL-DP框架同时利用加速度计和陀螺仪数据，在HAR准确率上取得了显著提升。评估包括对传统联邦学习与我们FSL框架的详细比较，结果表明FSL框架在准确率和损失指标上均优于FL模型。此外，我们研究了DP机制在不同数据设置下的隐私-性能权衡，强调了隐私保证与模型准确率之间的平衡。结果还表明，与传统FL相比，我们的FSL框架在每轮训练中实现了更快的通信时间，进一步凸显了其效率与有效性。本研究提供了有价值的见解，并在真实数据集上测试了这一新颖框架。