We consider decentralized machine learning over a network where the training data is
distributed across $ n $ agents, each of which can compute stochastic model updates on …

To mitigate the privacy leakages and communication burdens of Federated Learning (FL),
decentralized FL (DFL) discards the central server and each client only communicates with …

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 …

To enable large-scale machine learning in bandwidth-hungry environments such as
wireless networks, significant progress has been made recently in designing communication …

This paper describes a novel algorithmic framework to minimize a finite-sum of functions
available over a network of nodes. The proposed framework, that we call GT-VR, is …

Gradient tracking (GT) is an algorithm designed for solving decentralized optimization
problems over a network (such as training a machine learning model). A key feature of GT is …

We study solution methods for (strongly-) convex-(strongly)-concave Saddle-Point Problems
(SPPs) over networks of two type--master/workers (thus centralized) architectures and mesh …

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

This paper considers the decentralized convex optimization problem, which has a wide
range of applications in large-scale machine learning, sensor networks, and control theory …

This paper considers decentralized minimization of N:=nm smooth nonconvex cost functions
equally divided over a directed network of n nodes. Specifically, we describe a stochastic …