# Robust Group LASSO Over Decentralized Networks

## TL;DR

This paper develops decentralized algorithms for robustly recovering group sparse signals over multi-agent networks with sparse errors, using dynamic consensus strategies to replace centralized processing.

## Contribution

It introduces a decentralized approach for robust group LASSO signal recovery that avoids reliance on a central fusion center, utilizing dynamic average consensus techniques.

## Key findings

- Algorithms effectively recover signals in simulations.
- Decentralized method matches centralized performance.
- Dynamic consensus enables real-time tracking.

## Abstract

This paper considers the recovery of group sparse signals over a multi-agent network, where the measurements are subject to sparse errors. We first investigate the robust group LASSO model and its centralized algorithm based on the alternating direction method of multipliers (ADMM), which requires a central fusion center to compute a global row-support detector. To implement it in a decentralized network environment, we then adopt dynamic average consensus strategies that enable dynamic tracking of the global row-support detector. Numerical experiments demonstrate the effectiveness of the proposed algorithms.