14 citations · 24 across the 6 of their papers we have counts for
6 papers
Semantic-Aware Adaptive Video Streaming Using Latent Diffusion Models for Wireless Networks
Zijiang Yan, Jianhua Pei, Hongda Wu +2
This paper proposes a novel Semantic Communication (SemCom) framework for real-time adaptive-bitrate video streaming by integrating Latent Diffusion Models (LDMs) within the FFmpeg…
Straggler-resilient Federated Learning: Tackling Computation Heterogeneity with Layer-wise Partial Model Training in Mobile Edge Network
Hongda Wu, Ping Wang, C V Aswartha Narayana
Federated Learning (FL) enables many resource-limited devices to train a model collaboratively without data sharing. However, many existing works focus on model-homogeneous FL, whe…
A Deep Reinforcement Learning-Based Caching Strategy for IoT Networks with Transient Data
Hongda Wu, Ali Nasehzadeh, Ping Wang
The Internet of Things (IoT) has been continuously rising in the past few years, and its potentials are now more apparent. However, transient data generation and limited energy res…
An Ultra-Reliable Low-Latency Non-Binary Polar Coded SCMA Scheme
Shufeng Li, Mingyu Cai, Libiao Jin +3
The joint transmission scheme of polar codes and sparse code multiple access (SCMA) has been regarded as a promising technology for future wireless communication systems. However,…
Node Selection Toward Faster Convergence for Federated Learning on Non-IID Data
Hongda Wu, Ping Wang
Federated Learning (FL) is a distributed learning paradigm that enables a large number of resource-limited nodes to collaboratively train a model without data sharing. The non-inde…
Fast-Convergent Federated Learning with Adaptive Weighting
Hongda Wu, Ping Wang
Federated learning (FL) enables resource-constrained edge nodes to collaboratively learn a global model under the orchestration of a central server while keeping privacy-sensitive…