Deep learning models are widely being employed for object detection due to their high
performance. However, the majority of applications that require object detection are …

This study concentrates on deep learning-based lightweight object detection models on
edge devices. Designing such lightweight object recognition models is more difficult than …

Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that
enables in-situ model training and testing on edge data. Despite having the same end goals …

In recent times, we assist to an ever growing diffusion of smart medical sensors and Internet
of things devices that are heavily changing the way healthcare is approached worldwide. In …

Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial
intelligence (AI), including computer vision, natural language processing, and speech …

As mobile devices continuously generate streams of images and videos, a new class of
mobile deep vision applications are rapidly emerging, which usually involve running deep …

With the recent trend of on-device deep learning, inference latency has become a crucial
metric in running Deep Neural Network (DNN) models on various mobile and edge devices …

We present DarkneTZ, a framework that uses an edge device's Trusted Execution
Environment (TEE) in conjunction with model partitioning to limit the attack surface against …

Federated learning (FL) is an emerging, privacy-preserving machine learning paradigm,
drawing tremendous attention in both academia and industry. A unique characteristic of FL …

Machine Learning methods are playing a vital role in combating ever-evolving threats in the
cybersecurity domain. Explanation methods that shed light on the decision process of black …