用混合联邦学习提升穿戴设备活动识别准确率与隐私保护
FED-HARGPT: A Hybrid Centralized-Federated Approach of a Transformer-based Architecture for Human Context Recognition
- 结合中心化预训练与联邦学习，构建Transformer模型
- 非独立同分布数据下仍达中心化模型性能95%以上
- 适合注重隐私的可穿戴健康监测应用
本研究提出一种基于Transformer架构的混合中心化-联邦学习方法，用于人体活动识别（HAR）。随着智能手机和可穿戴设备的普及，大量来自惯性传感器的私密数据被生成，可用于对静止、睡眠、行走等行为进行隐蔽监测。研究利用手机传感器数据部署HAR技术，并在Flower框架下采用联邦学习训练从中心化基线模型衍生的联邦模型。实验结果表明，所提出的混合方法在非独立同分布（non-IID）数据场景下显著提升了HAR模型的准确率与鲁棒性，同时有效保护数据隐私。联邦学习设置表现接近中心化模型，在真实应用场景中实现了数据隐私与模型性能的平衡。
原文摘要 · Abstract (English)
The study explores a hybrid centralized-federated approach for Human Activity Recognition (HAR) using a Transformer-based architecture. With the increasing ubiquity of edge devices, such as smartphones and wearables, a significant amount of private data from wearable and inertial sensors is generated, facilitating discreet monitoring of human activities, including resting, sleeping, and walking. This research focuses on deploying HAR technologies using mobile sensor data and leveraging Federated Learning within the Flower framework to evaluate the training of a federated model derived from a centralized baseline. The experimental results demonstrate the effectiveness of the proposed hybrid approach in improving the accuracy and robustness of HAR models while preserving data privacy in a non-IID data scenario. The federated learning setup demonstrated comparable performance to centralized models, highlighting the potential of federated learning to strike a balance between data privacy and model performance in real-world applications.
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