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Found 6 papers, 0 papers with code
Diff-GO: Diffusion Goal-Oriented Communications to Achieve Ultra-High Spectrum Efficiency
This work presents an ultra-efficient communication design by utilizing generative AI-based on diffusion models as a specific example of the general goal-oriented communication framework.
A Principled Hierarchical Deep Learning Approach to Joint Image Compression and Classification
Our goal is to optimize DL models such that the encoder latent requires low channel bandwidth while still delivers feature information for high classification accuracy.
PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learning
Recent advances of generative learning models are accompanied by the growing interest in federated learning (FL) based on generative adversarial network (GAN) models.
Generative Adversarial Network
Personalized Federated Learning
UFed-GAN: A Secure Federated Learning Framework with Constrained Computation and Unlabeled Data
To satisfy the broad applications and insatiable hunger for deploying low latency multimedia data classification and data privacy in a cloud-based setting, federated learning (FL) has emerged as an important learning paradigm.
PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy
Our analysis demonstrates the convergence and privacy benefits of the proposed PS-FEdGAN framework.
RME-GAN: A Learning Framework for Radio Map Estimation based on Conditional Generative Adversarial Network
A practical goal is to estimate fine-resolution radio maps from sparse radio strength measurements.
