Many of the machine learning tasks are focused on centralized learning (CL), which requires
the transmission of local datasets from the clients to a parameter server (PS) leading to a …

The rapid advancement of quantum computing has pushed classical designs into the
quantum domain, breaking physical boundaries for computing-intensive and data-hungry …

Federated learning is a training paradigm according to which a server-based model is
cooperatively trained using local models running on edge devices and ensuring data …

Nowadays, with the rising number of sensor signals in sectors such as healthcare and
industry, the problem of multivariate time series classification (MTSC) is getting increasingly …

Deep neural networks have established themselves as one of the most promising machine
learning techniques. Training such models at large scales is often parallelized, giving rise to …

With the rapid growth in the volume of data sets, models, and devices in the domain of deep
learning, there is increasing attention on large-scale distributed deep learning. In contrast to …

Deep learning and computer vision revolutionized a new method to automate medical
image diagnosis. However, to achieve reliable and state-of-the-art performance, vision …

Expert parallelism has been introduced as a strategy to distribute the computational
workload of sparsely-gated mixture-of-experts (MoE) models across multiple computing …

The power of artificial intelligence (AI) models originates with sophisticated model
architecture as well as the sheer size of the model. These large-scale AI models impose new …

The size of neural networks in deep learning techniques is increasing and varies
significantly according to the requirements of real-life applications. The increasing network …