Joint Quality Evaluation, Model Splitting and Resource Provisioning for Split Edge Learning

Edge learning (EL) is an end-edge collaborative learning paradigm that enables numerous edge devices to participate in model training and data analysis, opening countless opportunities to enable edge intelligence. As is a promising EL approach, split edge learning (SPEL) alleviates the computation and communication overhead on resource-constrained devices via offloading part of the machine learning (ML) model to the edge server for cooperative training. Nevertheless, due to the system and statistical heterogeneity of the edge environment, naively using existing SPEL methods brings significantly time-consuming and accuracy degradation. Specifically, system heterogeneity causes intolerable time costs in each training round, while statistical heterogeneity further results in weight divergence and more training rounds to achieve global convergence. Motivated by this issue, this paper designs an efficient SPEL scheme to minimize the total time cost of participating devices. Specifically, we propose a novel SPEL framework and formulate the edge learning cost minimization (ELCM) problem that involves jointly optimizing model splitting and resource provisioning. We design OL-MG, i.e., OnLine Model Splitting and Resource Provisioning Game scheme, to solve the ELCM problem. In OL-MG, we first transform and decompose the original ELCM into two subproblems based on data quality evaluation. Second, we determine the optimal resource provisioning of Sub-problem1 with a given model splitting decision, based on which optimal model splitting of Sub-problem2 is modeled as a potential game. Then, we propose a decentralized algorithm to find a Nash equilibrium (NE) solution for the ELCM problem. Experimental results from both hardware prototype and simulation demonstrate that OL-MG outperforms the state-of-the-art methods, achieving up to 3.1x training cost savings and 40% accuracy improvement.