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

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

When the data is distributed across multiple servers, lowering the communication cost
between the servers (or workers) while solving the distributed learning problem is an …

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

The training efficiency of complex deep learning models can be significantly improved
through the use of distributed optimization. However, this process is often hindered by a …

The Alternating Directions Methods of Multipliers (ADMM) has witnessed a resurgence of
interest over the past few years fueled by the ever increasing demand for scalable …

This article proposes a communication-efficient decentralized deep learning algorithm,
coined layer-wise federated group ADMM (L-FGADMM). To minimize an empirical risk …

Gradient quantization is an emerging technique in reducing communication costs in
distributed learning. Existing gradient quantization algorithms often rely on engineering …