no code implementations • 13 Nov 2023 • Achintha Wijesinghe, Songyang Zhang, Suchinthaka Wanninayaka, Weiwei Wang, Zhi Ding
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.
no code implementations • 30 Oct 2023 • Siyu Qi, Achintha Wijesinghe, Lahiru D. Chamain, Zhi Ding
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.
no code implementations • 23 Aug 2023 • Achintha Wijesinghe, Songyang Zhang, Zhi Ding
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
no code implementations • 10 Aug 2023 • Achintha Wijesinghe, Songyang Zhang, Siyu Qi, Zhi Ding
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.
no code implementations • 19 May 2023 • Achintha Wijesinghe, Songyang Zhang, Zhi Ding
Our analysis demonstrates the convergence and privacy benefits of the proposed PS-FEdGAN framework.
no code implementations • 24 Dec 2022 • Songyang Zhang, Achintha Wijesinghe, Zhi Ding
A practical goal is to estimate fine-resolution radio maps from sparse radio strength measurements.