  Federated Learning (FL) has received a significant amount of attention in the
industry and research community due to its capability of keeping data on local
devices. To aggregate the gradients of local models to train the global model,
existing works require that the global model and the local models are the same.
However, Internet of Things (IoT) devices are inherently diverse regarding
computation speed and onboard memory. In this paper, we propose an FL framework
targeting the heterogeneity of IoT devices. Specifically, local models are
compressed from the global model, and the gradients of the compressed local
models are used to update the global model. We conduct preliminary experiments
to illustrate that our framework can facilitate the design of IoT-aware FL.
