  Deploying deep convolutional neural network (CNN) models on ubiquitous
Internet of Things (IoT) devices has attracted much attention from industry and
academia since it greatly facilitates our lives by providing various
rapid-response services. Due to the limited resources of IoT devices,
cloud-assisted training of CNN models has become the mainstream. However, most
existing related works suffer from a large amount of model parameter
transmission and weak model robustness. To this end, this paper proposes a
cloud-assisted CNN training framework with low model parameter transmission and
strong model robustness. In the proposed framework, we first introduce MonoCNN,
which contains only a few learnable filters, and other filters are
nonlearnable. These nonlearnable filter parameters are generated according to
certain rules, i.e., the filter generation function (FGF), and can be saved and
reproduced by a few random seeds. Thus, the cloud server only needs to send
these learnable filters and a few seeds to the IoT device. Compared to
transmitting all model parameters, sending several learnable filter parameters
and seeds can significantly reduce parameter transmission. Then, we investigate
multiple FGFs and enable the IoT device to use the FGF to generate multiple
filters and combine them into MonoCNN. Thus, MonoCNN is affected not only by
the training data but also by the FGF. The rules of the FGF play a role in
regularizing the MonoCNN, thereby improving its robustness. Experimental
results show that compared to state-of-the-art methods, our proposed framework
can reduce a large amount of model parameter transfer between the cloud server
and the IoT device while improving the performance by approximately 2.2% when
dealing with corrupted data. The code is available at
https://github.com/evoxlos/mono-cnn-pytorch.
