  In-home health monitoring has attracted great attention for the ageing
population worldwide. With the abundant user health data accessed by Internet
of Things (IoT) devices and recent development in machine learning, smart
healthcare has seen many successful stories. However, existing approaches for
in-home health monitoring do not pay sufficient attention to user data privacy
and thus are far from being ready for large-scale practical deployment. In this
paper, we propose FedHome, a novel cloud-edge based federated learning
framework for in-home health monitoring, which learns a shared global model in
the cloud from multiple homes at the network edges and achieves data privacy
protection by keeping user data locally. To cope with the imbalanced and
non-IID distribution inherent in user's monitoring data, we design a generative
convolutional autoencoder (GCAE), which aims to achieve accurate and
personalized health monitoring by refining the model with a generated
class-balanced dataset from user's personal data. Besides, GCAE is lightweight
to transfer between the cloud and edges, which is useful to reduce the
communication cost of federated learning in FedHome. Extensive experiments
based on realistic human activity recognition data traces corroborate that
FedHome significantly outperforms existing widely-adopted methods.
