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The rapid expansion of edge devices and Internet of Things (IoT) continues to heighten the demand for data transport under limited spectrum resources. The goal-oriented communications (GO-COM), unlike traditional communication systems designed for bit-level accuracy, prioritizes more critical information for specific application goals at the receiver. To improve the efficiency of generative learning models for GOCOM, this work introduces a novel noise-restricted diffusion based GO-COM (Diff-GOn) framework for reducing bandwidth overhead while preserving the media quality at the receiver. Specifically, we propose an innovative Noise-Restricted Forward Diffusion (NR-FD) framework to accelerate model training and reduce the computation burden for diffusion-based GO-COMs by leveraging a pre-sampled pseudo-random noise bank (NB). Moreover, we design an early stopping criterion for improving computational efficiency and convergence speed, allowing high quality generation in fewer training steps. Our experimental results demonstrate superior perceptual quality of data transmission at a reduced bandwidth usage and lower computation, making Diff-GOn well-suited for real-time communications and downstream applications. 

Federated Learning (FL) has emerged as an effective paradigm for distributed learning systems owing to its strong potential in exploiting underlying data characteristics while preserving data privacy. In cases of practical data heterogeneity among FL clients in many Internet-of-Things (IoT) applications over wireless networks, however, existing FL frameworks still face challenges in capturing the overall feature properties of local client data that often exhibit disparate distributions. One approach is to apply generative adversarial networks (GANs) in FL to address data heterogeneity by integrating GANs to regenerate anonymous training data without exposing original client data to possible eavesdropping. Despite some successes, existing GAN-based FL frameworks still incur high communication costs and elicit other privacy concerns, limiting their practical applications. To this end, this work proposes a novel FL framework that only applies partial GAN model sharing. This new PS-FedGAN framework effectively addresses heterogeneous data distributions across clients and strengthens privacy preservation at reduced communication costs, especially over wireless networks. Our analysis demonstrates the convergence and privacy benefits of the proposed PS-FEdGAN framework. Through experimental results based on several well-known benchmark datasets, our proposed PS-FedGAN demonstrates strong potential to tackle FL under heterogeneous (non-IID) client data distributions, while improving data privacy and lowering communication overhead.