Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …

We consider the problem of decentralized consensus optimization, where the sum of n
smooth and strongly convex functions are minimized over n distributed agents that form a …

We consider a decentralized learning problem, where a set of computing nodes aim at
solving a non-convex optimization problem collaboratively. It is well-known that …

In distributed optimization and machine learning, multiple nodes coordinate to solve large
problems. To do this, the nodes need to compress important algorithm information to bits so …

We study distributed optimization problems over a network when the communication
between the nodes is constrained, and therefore, information that is exchanged between the …

Information compression is essential to reduce communication cost in distributed
optimization over peer-to-peer networks. This article proposes a communication-efficient …

Although the field of distributed optimization is well developed, relevant literature focused on
the application of distributed optimization to multi-robot problems is limited. This survey …

Communication efficiency is a major bottleneck in the applications of distributed networks.
To address the problem, the problem of quantized distributed optimization has attracted a lot …

In this article, we consider distributed nonconvex optimization with the cost functions being
distributed over agents. Noting that information compression is a key tool to reduce the …

This paper studies distributed algorithms for (strongly convex) composite optimization
problems over mesh networks, subject to quantized communications. Instead of focusing on …