Gossip algorithms are attractive for in-network processing in sensor networks because they
do not require any specialized routing, there is no bottleneck or single point of failure, and …

The paper studies average consensus with random topologies (intermittent links) and noisy
channels. Consensus with noise in the network links leads to the bias-variance dilemma …

The paper studies distributed static parameter (vector) estimation in sensor networks with
nonlinear observation models and noisy intersensor communication. It introduces separably …

Motivated by applications to wireless sensor, peer-to-peer, and ad hoc networks, we study
distributed broadcasting algorithms for exchanging information and computing in an …

The paper studies the problem of distributed average consensus in sensor networks with
quantized data and random link failures. To achieve consensus, dither (small noise) is …

We consider the problem of decentralized consensus optimization, where the sum of n
smooth and strongly convex functions are minimized over n distributed agents that form a …

In this paper, we develop algorithms for distributed computation of averages of the node
data over networks with bandwidth/power constraints or large volumes of data. Distributed …

We consider a decentralized learning problem, where a set of computing nodes aim at
solving a non-convex optimization problem collaboratively. It is well-known that …

This paper considers the average consensus problem on a network of digital links, and
proposes a set of algorithms based on pairwise “gossip” communications and updates. We …

This work presents a contribution to the solution of the average agreement problem on a
network with quantized links. Starting from the well‐known linear diffusion algorithm, we …