In distributed optimization of multi-agent systems, agents cooperate to minimize a global
function which is a sum of local objective functions. Motivated by applications including …

Federated learning (FL) becomes popular and has shown great potentials in training large-
scale machine learning (ML) models without exposing the owners' raw data. In FL, the data …

In this paper, we study the problem of distributed multi-agent optimization over a network,
where each agent possesses a local cost function that is smooth and strongly convex. The …

In cross-silo federated learning (FL), organizations cooperatively train a global model with
their local data. The organizations, however, may be heterogeneous in terms of their …

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 …

Recently, the decreasing cost of storage technologies and the emergence of economy-
driven mechanisms for energy exchange are contributing to the spread of energy …

With the development of big data and artificial intelligence, distributed optimization has
emerged as an indispensable tool for solving large-scale problems. In particular, the multi …

Distributed optimization provides a framework for deriving distributed algorithms for a variety
of multi-robot problems. This tutorial constitutes the first part of a two-part series on …

Distributed optimization consists of multiple computation nodes working together to minimize
a common objective function through local computation iterations and network-constrained …

Although the field of distributed optimization is well developed, relevant literature focused on
the application of distributed optimization to multi-robot problems is limited. This survey …