Dynamic Optimal Coding and Scheduling for Distributed Learning over Wireless Edge Networks

This paper proposes a novel framework that can effectively address key challenges for the development of distributed learning over wireless edge networks. In particular, we first introduce a highly effective distributed learning model leveraging the most recent advanced coded distributed computing algorithm together with collaborative computing resources from wireless edge nodes to securely and effectively execute learning tasks. To minimize the average delay of learning tasks, the coding and scheduling policies must be jointly optimized. However, determining the optimal coding scheme together with the optimal edge nodes for different learning tasks is NP-hard due to the dynamics and uncertainty of the wireless environment and straggling problems at the computing nodes. Thus, we develop a highly effective approach utilizing advances of both reinforcement learning algorithms and the dueling network architecture to quickly find the optimal coding scheme together with the best edge nodes for different learning tasks without requiring completed information about the surrounding environment and straggling parameters in advance. Through extensive simulation results, we show that our proposed framework can reduce the average delay for the whole system up to 66% compared with other conventional learning and optimization approaches.