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 …

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 …

In this paper, we focus on solving the decentralized optimization problem of minimizing the
sum of n objective functions over a multi-agent network. The agents are embedded in an …

In the last few years, the theory of decentralized distributed convex optimization has made
significant progress. The lower bounds on communications rounds and oracle calls have …

Decentralized optimization over time-varying graphs has been increasingly common in
modern machine learning with massive data stored on millions of mobile devices, such as in …

Consider a network of N decentralized computing agents collaboratively solving a
nonconvex stochastic composite problem. In this work, we propose a single-loop algorithm …

We study distributed (strongly convex) optimization problems over a network of agents, with
no centralized nodes. The loss functions of the agents are assumed to be similar, due to …

Emerging applications in multiagent environments such as internet-of-things, networked
sensing, autonomous systems, and federated learning, call for decentralized algorithms for …

Personalized federated learning (PFL) aims to produce the greatest personalized model for
each client to face an insurmountable problem--data heterogeneity in real FL systems …

Abstract Personalized Federated Learning (PFL) is proposed to find the greatest
personalized models for each client. To avoid the central failure and communication …