To make machine learning (ML) sustainable and apt to run on the diverse devices where
relevant data is, it is essential to compress ML models as needed, while still meeting the …

Deep learning (DL) training or retraining on an edge computing network is promising due to
its local computation advantage over the cloud. Data and model parallel DL training is a …

With the emergence of edge devices along with their local computation advantage over the
cloud, distributed deep learning (DL) training on edge nodes becomes promising. In such a …

Model compression is a fundamental tool to execute machine learning (ML) tasks on the
diverse set of devices populating current-and next-generation networks, thereby exploiting …