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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.
In the last few years, distributed machine learning has been usually executed over heterogeneous networks such as a local area network within a multi-tenant cluster or a wide area network connecting data centers and edge clusters. In these heterogene
The present work introduces the hybrid consensus alternating direction method of multipliers (H-CADMM), a novel framework for optimization over networks which unifies existing distributed optimization approaches, including the centralized and the dec
We present a novel framework, called balanced overlay networks (BON), that provides scalable, decentralized load balancing for distributed computing using large-scale pools of heterogeneous computers. Fundamentally, BON encodes the information about
Many decentralized online social networks (DOSNs) have been proposed due to an increase in awareness related to privacy and scalability issues in centralized social networks. Such decentralized networks transfer processing and storage functionalities
This paper develops a fully discrete soft thresholding polynomial approximation over a general region, named Lasso hyperinterpolation. This approximation is an $ell_1$-regularized discrete least squares approximation under the same conditions of hype