Nowadays, the training data and neural network models are getting increasingly large. The
training time of deep learning will become unbearably long on a single machine. To reduce …

Communication efficiency has been widely recognized as the bottleneck for large-scale
decentralized machine learning applications in multi-agent or federated environments. To …

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

We consider the distributed optimization problem where agents, each possessing a local
cost function, collaboratively minimize the average of the cost functions over a connected …

In this paper, we propose two communication-efficient decentralized optimization algorithms
over a general directed multi-agent network. The first algorithm, termed Compressed Push …

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 …

In this article, we consider a strongly convex finite-sum minimization problem over a
decentralized network and propose a communication-efficient decentralized Newton's …

Stochastic distributed optimization methods that solve an optimization problem over a multi-
agent network have played an important role in a variety of large-scale signal processing …

Decentralized optimization and communication compression have exhibited their great
potential in accelerating distributed machine learning by mitigating the communication …