Zhu Han

Federated Learning for Digital Twin-Based Vehicular Networks: Architecture and Challenges

2022 · arXiv (Cornell University)

Emerging intelligent transportation applications, such as accident reporting, lane change assistance, collision avoidance, and infotainment, will be based on diverse requirements (e.g., latency, reliability, quality of physical experience). To fulfill such requirements, there is a …

Physical-layer Adversarial Robustness for Deep Learning-based Semantic Communications

2023 · arXiv (Cornell University)

End-to-end semantic communications (ESC) rely on deep neural networks (DNN) to boost communication efficiency by only transmitting the semantics of data, showing great potential for high-demand mobile applications. We argue that central to the success …

Protecting Personalized Trajectory with Differential Privacy under Temporal Correlations

2024 · arXiv (Cornell University)

Location-based services (LBSs) in vehicular ad hoc networks (VANETs) offer users numerous conveniences. However, the extensive use of LBSs raises concerns about the privacy of users' trajectories, as adversaries can exploit temporal correlations between different …

Swin Transformer-Based Dynamic Semantic Communication for Multi-User With Different Computing Capacity

2024 · IEEE Transactions on Vehicular Technology

Semantic communication has gained significant attention from researchers as a promising technique to replace conventional communication in the next generation of communication systems, primarily due to its ability to reduce communication costs. However, little literature …

Learning Tree-based Deep Model for Recommender Systems

2018

Model-based methods for recommender systems have been studied extensively in recent years. In systems with large corpus, however, the calculation cost for the learnt model to predict all user-item preferences is tremendous, which makes full …