Coded Stochastic ADMM for Decentralized Consensus Optimization With Edge Computing

Big data, including applications with high security requirements, are often collected and stored on multiple hetero- geneous devices, such as mobile devices, drones, and vehicles. Due to the limitations of communication costs and security require- ments, it is of paramount importance to analyze information in a decentralized manner instead of aggregating data to a fusion center. To train large-scale machine learning models, edge/fog computing is often leveraged as an alternative to centralized learning. We consider the problem of learning model param- eters in a multiagent system with data locally processed via distributed edge nodes. A class of minibatch stochastic alter- nating direction method of multipliers (ADMMs) algorithms is explored to develop the distributed learning model. To address two main critical challenges in distributed learning systems, i.e., communication bottleneck and straggler nodes (nodes with slow responses), error-control-coding-based stochastic incremen- tal ADMM is investigated