  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.
