In this paper, we propose a communication-efficiently decentralized machine learning
framework that solves a consensus optimization problem defined over a network of inter …

In this paper, we propose a communication-efficiently decentralized machine learning
framework that solves a consensus optimization problem defined over a network of inter …

In this article, we propose a communication-efficient decentralized machine learning (ML)
algorithm, coined quantized group ADMM (Q-GADMM). To reduce the number of …

The challenge of communication-efficient distributed optimization has attracted attention in
recent years. In this paper, a communication efficient algorithm, called ordering-based …

In this paper, we propose a fast, privacy-aware, and communication-efficient decentralized
framework to solve the distributed machine learning (DML) problem. The proposed …

Network-consensus-based decentralized learning optimization algorithms have attracted a
significant amount of attention in recent years due to their rapidly growing applications …

In distributed learning and optimization, a network of multiple computing units coordinates to
solve a large-scale problem. This article focuses on dynamic optimization over a …

The present work introduces the hybrid consensus alternating direction method of multipliers
(H-CADMM), a novel framework for optimization over networks which unifies existing …

Consensus-based decentralized stochastic gradient descent (D-SGD) is a widely adopted
algorithm for decentralized training of machine learning models across networked agents. A …

We consider the problem of training a given machine learning model by decentralized
parallel stochastic gradient descent over training data distributed across multiple nodes …