Human Activity Recognition (HAR) training data is often privacy-sensitive or held by non-cooperative entities. Federated Learning (FL) addresses such concerns by training ML models on edge clients. This work studies the impact of privacy in federated HAR at a user, environment, and sensor level. We show that the performance of FL for HAR depends on the assumed privacy level of the FL system and primarily upon the colocation of data from different sensors. By avoiding data sharing and assuming privacy at the human or environment level, as prior works have done, the accuracy decreases by 5-7%. However, extending this to the modality level and strictly separating sensor data between multiple clients may decrease the accuracy by 19-42%. As this form of privacy is necessary for the ethical utilisation of passive sensing methods in HAR, we implement a system where clients mutually train both a general FL model and a group-level one per modality. Our evaluation shows that this method leads to only a 7-13% decrease in accuracy, making it possible to build HAR systems with diverse hardware.

翻译：人类活动识别（HAR）的训练数据通常涉及隐私敏感信息或由非合作实体持有。联邦学习（FL）通过在边缘客户端训练机器学习模型来解决此类隐私问题。本研究从用户、环境和传感器三个层面探讨联邦HAR中的隐私影响。我们证明，FL在HAR中的性能取决于联邦学习系统假设的隐私等级，且主要受不同传感器数据共置情况的影响。如先前研究所示，若避免数据共享并在人类或环境层面假设隐私保护，准确率会下降5-7%。然而，若将隐私保护扩展至模态层面，并严格分离多个客户端之间的传感器数据，准确率可能下降19-42%。由于这种形式的隐私保护对于HAR中被动感知方法的伦理应用至关重要，我们实现了一种系统架构：客户端在训练通用联邦学习模型的同时，针对每种模态协同训练组级模型。评估结果表明，该方法仅导致7-13%的准确率下降，从而使得构建具有异构硬件的HAR系统成为可能。