Distributed Detection : Finite-time Analysis and Impact of Network Topology

09/30/2014
by   Shahin Shahrampour, et al.
0

This paper addresses the problem of distributed detection in multi-agent networks. Agents receive private signals about an unknown state of the world. The underlying state is globally identifiable, yet informative signals may be dispersed throughout the network. Using an optimization-based framework, we develop an iterative local strategy for updating individual beliefs. In contrast to the existing literature which focuses on asymptotic learning, we provide a finite-time analysis. Furthermore, we introduce a Kullback-Leibler cost to compare the efficiency of the algorithm to its centralized counterpart. Our bounds on the cost are expressed in terms of network size, spectral gap, centrality of each agent and relative entropy of agents' signal structures. A key observation is that distributing more informative signals to central agents results in a faster learning rate. Furthermore, optimizing the weights, we can speed up learning by improving the spectral gap. We also quantify the effect of link failures on learning speed in symmetric networks. We finally provide numerical simulations which verify our theoretical results.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/21/2017

An Online Optimization Approach for Multi-Agent Tracking of Dynamic Parameters in the Presence of Adversarial Noise

This paper addresses tracking of a moving target in a multi-agent networ...
research
04/02/2020

Event-Triggered Distributed Inference

We study a setting where each agent in a network receives certain privat...
research
03/11/2015

Switching to Learn

A network of agents attempt to learn some unknown state of the world dra...
research
05/17/2023

Set-Membership Filtering-Based Cooperative State Estimation for Multi-Agent Systems

In this article, we focus on the cooperative state estimation problem of...
research
07/05/2019

A New Approach to Distributed Hypothesis Testing and Non-Bayesian Learning: Improved Learning Rate and Byzantine-Resilience

We study a setting where a group of agents, each receiving partially inf...
research
11/06/2020

Communication-efficient Decentralized Local SGD over Undirected Networks

We consider the distributed learning problem where a network of n agents...
research
09/10/2013

Exponentially Fast Parameter Estimation in Networks Using Distributed Dual Averaging

In this paper we present an optimization-based view of distributed param...

Please sign up or login with your details

Forgot password? Click here to reset