TSGAN: A Lightweight Teacher-Student-Based GAN Framework for the Edge-Cloud Computing
Weitong Liao, Hao Wu, Zikun Zhang, Mohammed H. Alghamdi, Hammam M. AlGhamdi, Ligang He · 2025
Generative Adversarial Networks (GANs) have proven to be effective in generating synthesized data. However, their use often requires large amounts of data and sufficient computing power for model training. This presents significant challenges for GAN training in edge devices, which typically lack the necessary resources for GAN training. Additionally, the data collected by these devices often contain sensitive information that requires privacy preservation. The data volume may also be insufficient for GAN training, and the data collected by individual edge devices may have both common and unique features across the network. To address these challenges, we propose TSGAN, a lightweight GAN framework for Edge-Cloud computing. Coordinated by a Cloud server, TSGAN allows a network of resource-limited edge devices to train GAN models for privacy-preserving data generation. We propose a teacher-student model to enable edge devices to generate high-quality data. We also propose a novel deployment mechanism that facilitates effective distributed learning across edge devices and the Cloud server, while preventing the Cloud server generating the synthetic data from accessing the data collected by edge devices. Finally, we introduce a joint restraint learning function that enhances the effectiveness of learning unique features from data on individual edge devices. We conducted extensive experiments. The results have verified the effectiveness of TSGAN.