摘要
With the proliferation of end devices, such as smart?phones, wearable sensors and drones, an enor?mous amount of data is generated at the networkedge. This motivates the deployment of machine learning algorithms at the edge that exploit the data to train ar?tificial intelligence (AI) models for making intelligent deci?sions. Traditional machine learning procedures, including both training and inference, are carried out in a centralized da?ta center, thus requiring devices to upload their raw data to the center.
