74 citations · 182 across the 17 of their papers we have counts for
5 papers · 1 filter
Reinforcement Learning for Robust Header Compression under Model Uncertainty
Shusen Jing, Songyang Zhang, Zhi Ding
Robust header compression (ROHC), critically positioned between the network and the MAC layers, plays an important role in modern wireless communication systems for improving data…
PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learning
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. In the contex…
UFed-GAN: A Secure Federated Learning Framework with Constrained Computation and Unlabeled Data
Achintha Wijesinghe, Songyang Zhang, Siyu Qi +1
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)…
Radiomap Inpainting for Restricted Areas based on Propagation Priority and Depth Map
Songyang Zhang, Tianhang Yu, Brian Choi +2
Providing rich and useful information regarding spectrum activities and propagation channels, radiomaps characterize the detailed distribution of power spectral density (PSD) and a…
PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy
Achintha Wijesinghe, Songyang Zhang, Zhi Ding
Federated Learning (FL) has emerged as an effective learning paradigm for distributed computation owing to its strong potential in capturing underlying data statistics while preser…