  In this paper, we consider a large network containing many regions such that
each region is equipped with a worker with some data processing and
communication capability. For such a network, some workers may become
stragglers due to the failure or heavy delay on computing or communicating. To
resolve the above straggling problem, a coded scheme that introduces certain
redundancy for every worker was recently proposed, and a gradient coding
paradigm was developed to solve convex optimization problems when the network
has a centralized fusion center. In this paper, we propose an iterative
distributed algorithm, referred as Code-Based Distributed Gradient Descent
algorithm (CoDGraD), to solve convex optimization problems over distributed
networks. In each iteration of the proposed algorithm, an active worker shares
the coded local gradient and approximated solution of the convex optimization
problem with non-straggling workers at the adjacent regions only. In this
paper, we also provide the consensus and convergence analysis for the CoDGraD
algorithm and we demonstrate its performance via numerical simulations.

Systems and Control;Systems and Control;Optimization and Control