CLONE: Collaborative Learning on the Edges

The proliferation of edge computing technologies has boosted the development of new applications for a plethora of edge devices. However, many applications face privacy issues and bandwidth limitations. To solve these limitations, we propose a collaborative learning framework on the edges, named CLONE, which is steered by the real-world data sets collected from a large electric vehicle (EV) company and a grocery store of a shopping mall, respectively. We categorize two application scenarios for CLONE, i.e., CLONE in the training stage (CLONE_training) and CLONE in the inference stage (CLONE_inference). As to CLONE_training, we choose the failure prediction of EV battery and associated components as the first use case. While as for CLONE_inference, customer tracking in a grocery store is selected as another case study. In this work, the goal of the CLONE is to support real-time training and inference for connected vehicles and marketing intelligence services. Our experimental results on the EV data show that CLONE is able to reduce model training time without sacrificing algorithm performance. Furthermore, the experimental results on the video data from the grocery store reveal that CLONE is a useful approach to solve the multitarget multicamera tracking problem in a collaborative fashion.