Training deep learning models on in-home IoT sensory data is commonly used to recognise human activities. Recently, federated learning systems that use edge devices as clients to support local human activity recognition have emerged as a new paradigm to combine local (individual-level) and global (group-level) models. This approach provides better scalability and generalisability and also offers better privacy compared with the traditional centralised analysis and learning models. The assumption behind federated learning, however, relies on supervised learning on clients. This requires a large volume of labelled data, which is difficult to collect in uncontrolled IoT environments such as remote in-home monitoring. In this paper, we propose an activity recognition system that uses semi-supervised federated learning, wherein clients conduct unsupervised learning on autoencoders with unlabelled local data to learn general representations, and a cloud server conducts supervised learning on an activity classifier with labelled data. Our experimental results show that using a long short-term memory autoencoder and a Softmax classifier, the accuracy of our proposed system is higher than that of both centralised systems and semi-supervised federated learning using data augmentation. The accuracy is also comparable to that of supervised federated learning systems. Meanwhile, we demonstrate that our system can reduce the number of needed labels and the size of local models, and has faster local activity recognition speed than supervised federated learning does.
翻译:最近,使用边端设备作为客户支持当地人类活动的确认的联合会式学习系统,已成为将当地(个人一级)和全球(群体一级)模式结合起来的新范例。这种方法提供了更好的可缩放性和可普及性,并与传统的中央化分析和学习模式相比,提供了更好的隐私。但是,联合会式学习背后的假设依赖于客户的监督学习。这需要大量贴有标签的数据,难以在无节制的IOT环境中收集,如远程家庭监测。在本文件中,我们提议一个活动识别系统,使用半超级的Federal化学习,使客户在无标签的本地数据自动调整器上进行不受监督的学习,以学习一般表述,并提供一个云服务器监督地学习带有贴标签数据的活动分类器。我们的实验结果表明,使用长期的内存自动解析器和软体分类器,我们提议的系统的准确性高于中央化系统,而半超导式的Federal化系统则使用可比较的准确度学习系统。