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 …

Decentralized methods to solve finite-sum minimization problems are important in many
signal processing and machine learning tasks where the data samples are distributed …

Resource allocation and scheduling in multi-agent systems present challenges due to
complex interactions and decentralization. This survey paper provides a comprehensive …

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 …

Many modern large-scale machine learning problems benefit from decentralized and
stochastic optimization. Recent works have shown that utilizing both decentralized …

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 …