This paper surveys recent advances related to adaptation, learning, and optimization over
networks. Various distributed strategies are discussed that enable a collection of networked …

This work deals with the topic of information processing over graphs. The presentation is
largely self-contained and covers results that relate to the analysis and design of multi-agent …

We propose an adaptive diffusion mechanism to optimize global cost functions in a
distributed manner over a network of nodes. The cost function is assumed to consist of a …

This article proposes diffusion LMS strategies for distributed estimation over adaptive
networks that are able to exploit sparsity in the underlying system model. The approach …

Adaptive networks rely on in-network and collaborative processing among distributed
agents to deliver enhanced performance in estimation and inference tasks. Information is …

We study cooperative sensor network localization in a realistic scenario where the
underlying measurement errors more probably follow a non-Gaussian distribution; the …

Diffusion networks where nodes collaboratively estimate the parameters of stochastic
models from shared observations and other estimates have become an established …

In practice, when complex multi-agent networks are used for parameter estimation and
tracking, we often face the issue of spatial anisotropy of observing conditions, eg, different …

We address the problem of distributed estimation of a parameter from a set of noisy
observations collected by a sensor network, assuming that some sensors may be subject to …

Distributed machine learning is a problem of inferring a desired relation when the training
data is distributed throughout a network of agents (eg sensor networks, robot swarms, etc.) …