Federated learning (FL) is a distributed learning paradigm that leverages local updates and parameter sharing to address privacy concerns in Industrial Internet of Things (IIoT) environments. The presence of statistical heterogeneity among IIoT devices poses significant challenges for FL, impacting convergence and model performance. Although previous studies have attempted to mitigate this issue, a fundamental solution remains elusive. To address this, we propose a novel approach called federated multidiscriminators multigenerators generative adversarial network (FedMDMG-GAN), which employs a distributed generative adversarial network (GAN). IIoT devices concurrently train local generators and discriminators to generate data with global information. The server then aggregates parameters and redistributes them to the devices to refine local GANs. In addition, we introduce a proximal term for global aggregation to enhance convergence. Theoretical analysis suggests that the FedMDMG-GAN algorithm can asymptotically converge to a stable point. Qualitative assessments demonstrate that our method can generate images closely resembling real data with comprehensive global information. Quantitative results indicate that FedMDMG-GAN outperforms vanilla FL and state-of-the-art methods.