Human activity recognition (HAR) based on mobile sensors plays an important role in ubiquitous computing. However, the rise of data regulatory constraints precludes collecting private and labeled signal data from personal devices at scale. Federated learning has emerged as a decentralized alternative solution to model training, which iteratively aggregates locally updated models into a shared global model, therefore being able to leverage decentralized, private data without central collection. However, the effectiveness of federated learning for HAR is affected by the fact that each user has different activity types and even a different signal distribution for the same activity type. Furthermore, it is uncertain if a single global model trained can generalize well to individual users or new users with heterogeneous data. In this paper, we propose Meta-HAR, a federated representation learning framework, in which a signal embedding network is meta-learned in a federated manner, while the learned signal representations are further fed into a personalized classification network at each user for activity prediction. In order to boost the representation ability of the embedding network, we treat the HAR problem at each user as a different task and train the shared embedding network through a Model-Agnostic Meta-learning framework, such that the embedding network can generalize to any individual user. Personalization is further achieved on top of the robustly learned representations in an adaptation procedure. We conducted extensive experiments based on two publicly available HAR datasets as well as a newly created HAR dataset. Results verify that Meta-HAR is effective at maintaining high test accuracies for individual users, including new users, and significantly outperforms several baselines, including Federated Averaging, Reptile and even centralized learning in certain cases.
翻译:以移动传感器为基础的人类活动识别(HAR)基于移动传感器的人类活动识别(HAR)在无所不在的计算中起着重要作用。然而,数据监管限制的上升使得无法从规模上从个人设备收集私人和贴标签的信号数据。联邦学习已经成为一种分散化的替代模式,作为模式培训的替代方案,将当地更新的模型反复地汇总成一个共享的全球模型,从而能够在不集中收集的情况下利用分散的私人数据。然而,由于每个用户都有不同的活动类型,甚至同一活动类型有不同的信号发送方式,因此,联合会学习对HAR的有效性受到了影响。此外,如果一个经过培训的全球模型能够很好地向个人用户或带有不同数据的新用户推广信号数据。在本文件中,我们建议一个混合化的代表模式-HAR(MAHAR),即一个联合化的代表学习框架,其中信号嵌入的网络是元化的,而学习信号进一步输入每个用户的个性化分类网络。为了提高嵌入网络的代表性,我们把每个用户的问题当作一个不同的任务,通过一个共享的存储网络,包括通过一个基于模型的高级数据库的高级数据库的升级程序,这是我们所学到的、在进行的任何数据测试中实现的系统。