10 papers
Communication-Efficient Digital-Twin Coordination for Heterogeneous LLM Embodied Agents over Computing Power Networks
Nuocheng Yang, Sihua Wang, Zihan Chen +2
Embodied agent teams powered by heterogeneous large language models (LLMs) are being widely deployed in physical artificial intelligence such as smart factories, warehouses, and se…
A Query-Driven Communication-Efficient Digital Twins Design for Autonomous Driving
Nuocheng Yang, Longyu Zhou, Sihua Wang +2
Digital twins (DTs) have become a potential technology to perform risk-free simulation of physical entities for deterministic and high-reliability services in diverse scenarios suc…
Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuning
Nuocheng Yang, Yechen He, Sihua Wang +3
As large language models (LLMs) are increasingly deployed at the network edge to provide pervasive generative AI services, decentralized federated learning (DFL) provides a vital m…
Recursive Vision Transformer with Dynamic Depth and Width Adjustment for Resource-Efficient Image Semantic Communication
Zhilong Zhang, Xinhui Zhang, Gongyu Jin +3
Image semantic communication is a critical component in next-generation wireless communication systems. However, such systems typically suffer from large memory footprints and high…
Wireless Federated Multi-Task LLM Fine-Tuning via Sparse-and-Orthogonal LoRA
Nuocheng Yang, Sihua Wang, Ouwen Huan +3
Decentralized federated learning (DFL) based on low-rank adaptation (LoRA) enables mobile devices with multi-task datasets to collaboratively fine-tune a large language model (LLM)…
A Secure and Private Distributed Bayesian Federated Learning Design
Nuocheng Yang, Sihua Wang, Zhaohui Yang +3
Distributed Federated Learning (DFL) enables decentralized model training across large-scale systems without a central parameter server. However, DFL faces three critical challenge…