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 …

Decentralized stochastic optimization methods have gained a lot of attention recently, mainly
because of their cheap per iteration cost, data locality, and their communication-efficiency. In …

We consider decentralized stochastic optimization with the objective function (eg data
samples for machine learning tasks) being distributed over n machines that can only …

Federated Learning (FL) is popular for communication-efficient learning from distributed
data. To utilize data at different clients without moving them to the cloud, algorithms such as …

In this article, we focus on solving a distributed convex optimization problem in a network,
where each agent has its own convex cost function and the goal is to minimize the sum of …

This paper proposes a novel proximal-gradient algorithm for a decentralized optimization
problem with a composite objective containing smooth and nonsmooth terms. Specifically …

This paper considers the distributed optimization problem over a network, where the
objective is to optimize a global function formed by a sum of local functions, using only local …

Decentralized training of deep learning models is a key element for enabling data privacy
and on-device learning over networks. In realistic learning scenarios, the presence of …

Decentralized optimization to minimize a finite sum of functions, distributed over a network of
nodes, has been a significant area within control and signal-processing research due to its …

In this work, we consider the distributed optimization of non-smooth convex functions using a
network of computing units. We investigate this problem under two regularity …