1 citations · 2 across the 9 of their papers we have counts for
9 papers
HermesHFL: Incentive-Compatible Hierarchical Federated Unlearning for Dynamic LLM Fine-Tuning
Chenxi Sun, Minghui Liwang, Wusi He +5
Hierarchical federated unlearning (HFUL) for large language model (LLM) fine-tuning faces significant challenges due to hierarchical aggregation, dynamic client participation, and…
DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation
Yaoshen Yu, Minghui Liwang, Wenbo Zhu +5
Future intelligent transportation systems are envisioned to evolve toward a long-term mixed-autonomy paradigm, where human-driven vehicles (HVs) and autonomous vehicles (AVs) coexi…
Digital Twin-Assisted Task Offloading and Resource Allocation in ISAC-Enabled Internet of Vehicles
Shanhao Zhan, Zhang Liu, Lianfen Huang +4
The convergence of the Internet of vehicles (IoV) and 6G networks is driving the evolution of next-generation intelligent transportation systems. However, IoV networks face persist…
Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking
Zhang Liu, Dusit Niyato, Jiacheng Wang +4
Lyapunov optimization theory has recently emerged as a powerful mathematical framework for solving complex stochastic optimization problems by transforming long-term objectives int…
Joint Model Caching and Resource Allocation in Generative AI-Enabled Wireless Edge Networks
Zhang Liu, Hongyang Du, Lianfen Huang +2
With the rapid advancement of artificial intelligence (AI), generative AI (GenAI) has emerged as a transformative tool, enabling customized and personalized AI-generated content (A…
Two-Timescale Model Caching and Resource Allocation for Edge-Enabled AI-Generated Content Services
Zhang Liu, Hongyang Du, Xiangwang Hou +4
Generative AI (GenAI) has emerged as a transformative technology, enabling customized and personalized AI-generated content (AIGC) services. In this paper, we address challenges of…
DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach
Zhang Liu, Hongyang Du, Junzhe Lin +4
The rapid advancement of Artificial Intelligence (AI) has introduced Deep Neural Network (DNN)-based tasks to the ecosystem of vehicular networks. These tasks are often computation…
GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling over Dynamic Vehicular Clouds
Zhang Liu, Lianfen Huang, Zhibin Gao +3
Vehicular clouds (VCs) are modern platforms for processing of computation-intensive tasks over vehicles. Such tasks are often represented as directed acyclic graphs (DAGs) consisti…
RFID: Towards Low Latency and Reliable DAG Task Scheduling over Dynamic Vehicular Clouds
Zhang Liu, Minghui Liwang, Seyyedali Hosseinalipour +3
Vehicular cloud (VC) platforms integrate heterogeneous and distributed resources of moving vehicles to offer timely and cost-effective computing services. However, the dynamic natu…