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Acquiring downlink channel state information (CSI) at the base station is vital for optimizing performance in massive Multiple input multiple output (MIMO) Frequency-Division Duplexing (FDD) systems. While deep learning architectures have been successful in facilitating UE-side CSI feedback and gNB side recovery, the undersampling issue prior to CSI feedback is often overlooked. This issue, which arises from low-density pilot placement in current standards, results in significant aliasing effects in outdoor channels and consequently limits CSI recovery performance. To this end, this work introduces a new CSI upsampling framework at the gNB as a post-processing solution to address the gaps caused by undersampling. Leveraging the physical principles of discrete Fourier transform shifting theorem and multipath reciprocity, our framework effectively uses uplink CSI to mitigate aliasing effects. We further develop a learning based method that integrates the proposed algorithm with the Iterative Shrinkage-Thresholding Algorithm Net (ISTA-Net) architecture, enhancing our approach for non-uniform sampling recovery. Our numerical results show that both our rule-based and deep learning methods significantly outperform traditional interpolation techniques and current state-of-the-art approaches in terms of performance. 

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.