Deploying deep learning (DL) on mobile devices has been a notable trend in recent years.
To support fast inference of on-device DL, DL libraries play a critical role as algorithms and …

Deploying deep learning (DL) on mobile devices has been a notable trend in recent years.
To support fast inference of on-device DL, DL libraries play a critical role as algorithms and …

With smartphones' omnipresence in people's pockets, Machine Learning (ML) on mobile is
gaining traction as devices become more powerful. With applications ranging from visual …

Federated Learning (FL) allows edge devices to collaboratively learn a shared prediction
model while keeping their training data on the device, thereby decoupling the ability to do …

Semantic segmentation arises as the backbone of many vision systems, spanning from self-
driving cars and robot navigation to augmented reality and teleconferencing. Frequently …

Deep Learning (DL) models have been widely deployed on IoT devices with the help of
advancements in DL algorithms and chips. However, the limited resources of edge devices …

While a number of recent efforts have explored the use of" cloud offload" to enable deep
learning on IoT devices, these have not assumed the use of duty-cycled radios like BLE. We …

Edge devices can offload deep neural network (DNN) inference to the cloud to overcome
energy or processing constraints. Nevertheless, offloading adds communication delay …

In the vanilla federated learning (FL) framework, the central server distributes a globally
unified model to each client and uses labeled samples for training. However, in most cases …

Federated learning has been predominantly concerned with collaborative training of deep
networks from scratch, and especially the many challenges that arise, such as …