Towards a Federated Learning Framework for Heterogeneous Devices of Internet of Things
This addresses the challenge of applying FL to diverse IoT devices, but it appears incremental as it builds on existing FL methods by adding compression for heterogeneity.
The paper tackles the problem of federated learning (FL) on heterogeneous IoT devices by proposing a framework where local models are compressed from the global model, enabling gradient aggregation despite device diversity, with preliminary experiments showing it facilitates IoT-aware FL.
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.