We consider a general multi-agent convex optimization problem where the agents are to
collectively minimize a global objective function subject to a global inequality constraint, a …

We consider a distributed learning problem over multiple access channel (MAC) using a
large wireless network. The computation is made by the network edge and is based on …

An adaptive distributed strategy is developed based on incremental techniques. The
proposed scheme addresses the problem of linear estimation in a cooperative fashion, in …

We study dual-based algorithms for distributed convex optimization problems over networks,
where the objective is to minimize a sum Σ i= 1 mfi (z) of functions over in a network. We …

We consider the problem of distributed learning, where a network of agents collectively aim
to agree on a hypothesis that best explains a set of distributed observations of conditionally …

We study the problem of minimizing a sum of convex objective functions, where the
components of the objective are available at different nodes of a network and nodes are …

We study deterministic mean-location parameter estimation when only quantized versions of
the original observations are available, due to bandwidth constraints. When the dynamic …

Wireless sensor networks are capable of collecting an enormous amount of data. Often, the
ultimate objective is to estimate a parameter or function from these data, and such estimators …

In this paper, we propose a received signal strength indication-based distributed Bayesian
localization algorithm based on message passing to solve the approximate inference …

An incremental aggregated gradient method for minimizing a sum of continuously
differentiable functions is presented. The method requires a single gradient evaluation per …