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

Distributed deep learning (DL) has become prevalent in recent years to reduce training time
by leveraging multiple computing devices (eg, GPUs/TPUs) due to larger models and …

By enabling multiple agents to cooperatively solve a global optimization problem in the
absence of a central coordinator, decentralized stochastic optimization is gaining increasing …

In this article, we propose and analyze SParsified Action Regulated Quantized–Stochastic
Gradient Descent (SPARQ-SGD), a communication-efficient algorithm for decentralized …

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 provide a unifying framework for distributed convex optimization over time-varying
networks, in the presence of constraints and uncertainty, features that are typically treated …

In this paper, we propose and analyze SQuARM-SGD, a communication-efficient algorithm
for decentralized training of large-scale machine learning models over a network. In …

Based on the theories of inertial systems, a second-order accelerated neurodynamic
approach is designed to solve a distributed convex optimization with inequality and set …

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

This paper proposes distributed discrete-time algorithms to cooperatively solve an additive
cost optimization problem in multiagent networks. The striking feature lies in the use of only …