Distributed linear algebraic equation over networks, where nodes hold a part of problem data and cooperatively solve the via node-to-node communications, is basic distributed computation task receiving an increasing research attention. Communications network have stochastic nature, with both temporal spatial dependence due to link failures, packet dropouts, or node recreation, etc. In this arti...

Abstract Methods based on the alternating direction method of multipliers (ADMM) has attracted academic attention because its excellent convergence performance and potential application scenarios in many machine learning or optimization fields. However, classical distributed ADMM algorithm assumed ideal network communication, which do not consider impact delay computing performance. In this pap...

The aim of this work is to develop a fully-distributed algorithmic framework for training graph convolutional networks (GCNs). proposed method able exploit the meaningful relational structure input data, which are collected by set agents that communicate over sparse network topology. After formulating centralized GCN problem, we first show how make inference in distributed scenario where underl...