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.
@article{arxiv.2105.14675,
title = {Towards a Federated Learning Framework for Heterogeneous Devices of Internet of Things},
author = {Huanle Zhang and Jeonghoon Kim},
journal= {arXiv preprint arXiv:2105.14675},
year = {2021}
}