When gradient descent (GD) is scaled to many parallel computing servers (workers) for
large scale machine learning problems, its per-iteration computation time is limited by the …

Probabilistic programming languages (PPLs) are receiving widespread attention for
performing Bayesian inference in complex generative models. However, applications to …

With the prevalence of the Internet of Things (IoT) systems, smart cities comprise complex
networks, including sensors, actuators, appliances, and cyber services. The complexity and …

Most machine learning applications rely on centralized learning processes, opening up the
risk of exposure of their training datasets. While federated learning (FL) mitigates to some …

Classification of normal and malignant cells observed under a microscope is an essential
and challenging step in the development of a cost-effective computer-aided diagnosis tool …

Federated Learning (FL) is an emerging distributed machine learning (ML) technique that
enables in-situ model training and inference on decentralized edge devices. We propose …

With the increasing demand for large-scale training of machine learning models, consensus-
based distributed optimization methods have recently been advocated as alternatives to the …

With the increasing demand for large-scale training of machine learning models, fully
decentralized optimization methods have recently been advocated as alternatives to the …

In this paper, we investigate the impact of imbalanced data on the convergence of
distributed dual coordinate ascent in a tree network for solving an empirical loss …

This paper investigates the stochastic optimization problem focusing on developing scalable
parallel algorithms for deep learning tasks. Our solution involves a reformation of the …