6 papers
SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compression
Zehang Lin, Miao Yang, Haihan Zhu +9
The growing complexity of neural networks hinders the deployment of distributed machine learning on resource-constrained devices. Split learning (SL) offers a promising solution by…
Channel-Adaptive Edge AI: Maximizing Inference Throughput by Adapting Computational Complexity to Channel States
Jierui Zhang, Jianhao Huang, Kaibin Huang
\emph{Integrated communication and computation} (IC) has emerged as a new paradigm for enabling efficient edge inference in sixth-generation (6G) networks. However, the design…
LoLaFL: Low-Latency Federated Learning via Forward-only Propagation
Jierui Zhang, Jianhao Huang, Kaibin Huang
Federated learning (FL) has emerged as a widely adopted paradigm for enabling edge learning with distributed data while ensuring data privacy. However, the traditional FL with deep…
Generative Feature Imputing -- A Technique for Error-resilient Semantic Communication
Jianhao Huang, Qunsong Zeng, Hongyang Du +1
Semantic communication (SemCom) has emerged as a promising paradigm for achieving unprecedented communication efficiency in sixth-generation (6G) networks by leveraging artificial…
Visual Fidelity Index for Generative Semantic Communications with Critical Information Embedding
Jianhao Huang, Qunsong Zeng, Kaibin Huang
Generative semantic communication (Gen-SemCom) with large artificial intelligence (AI) model promises a transformative paradigm for 6G networks, which reduces communication costs b…
Ultra-Low-Latency Edge Intelligent Sensing: A Source-Channel Tradeoff and Its Application to Coding Rate Adaptation
Qunsong Zeng, Jianhao Huang, Zhanwei Wang +2
The forthcoming sixth-generation (6G) mobile network is set to merge edge artificial intelligence (AI) and integrated sensing and communication (ISAC) extensively, giving rise to t…