Privacy-aware offloading for training tasks of generative adversarial network in edge computing
Xiaolong Xu, Xihua Liu, Xiaochun Yin, Shoujin Wang, Quan Qi, Lianyong Qi
Abstract
Currently, the generative adversarial network (GAN), with complex training processes in the physical machine (PM), has achieved great priority in image generation , audio conversion, image translation, etc. To improve the training efficiency of GAN, the edge computing paradigm is accepted as an alternative of the PMs to accommodate the training tasks, that is, the training tasks are migrated to the edge nodes (ENs) for hosting. However, it is still a key challenge to keep the overall network performance (i.e., load balance , transmission time) and privacy protection of training tasks at the same time. To address this challenge, a privacy-aware task offloading method, named POM, is developed accordingly in this paper. First, improving the strength pareto evolutionary algorithm (SPEA2) is fully investigated to obtain the offloading strategies for collaboratively improving the training performance and privacy preservation . Then, the most balanced offloading strategy is acquired for training GAN. Eventually, systematic experiments indicate that POM achieves an optimal performance efficiently among the other representative benchmark methods.