On-device Deep Neural Network (DNN) training has been recognized as crucial for privacy-
preserving machine learning at the edge. However, the intensive training workload and …

With the increased penetration and proliferation of Internet of Things (IoT) devices, there is a
growing trend towards distributing the power of deep learning (DL) across edge devices …

On-device deep neural network (DNN) training holds the potential to enable a rich set of
privacy-aware and infrastructure-independent personalized mobile applications. However …

It is usually infeasible to fit and train an entire large deep neural network (DNN) model using
a single edge device due to the limited resources. To facilitate intelligent applications across …

Performing inference tasks of deep learning applications on IoT edge devices ensures
privacy of input data and can result in shorter latency when compared to a cloud solution. As …

On-device learning is a promising technique for emerging privacy-preserving machine
learning paradigms. However, through quantitative experiments, we find that commodity …

Training and inferencing phases of Deep Learning (DL) are compute-intensive that require
substantial amount of cloud-hosted resources. However, real-time needs of some edge …

Recent years have witnessed deep neural networks (DNNs) become the de facto tool in
many applications such as image classification and speech recognition. But significant …

Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT)
devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent …

Classical federated learning methods aggregate decentralized data from different devices
into a central location for efficient training. However, these approaches raise significant …