4 citations · 4 across the 6 of their papers we have counts for
6 papers
Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learning
Zifan Zhang, Minghong Fang, Dianwei Chen +5
Federated reinforcement learning (FRL) is crucial for enabling collaborative learning across multiple agents without sharing raw data, thereby enhancing privacy and scalability in…
Customized Generative AI Agent for Transportation Engineering Practice: A Development and Continued Pre-training Guideline
Dianwei Chen, Yuan-Zheng Lei, Zifan Zhang +2
Recent advancements in generative artificial intelligence (AI) and large language models (LLMs) have shown significant promise in automating complex reasoning, summarization, and q…
Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security
Zifan Zhang, Minghong Fang, Dianwei Chen +2
Digital network twins (DNTs) are virtual representations of physical networks, designed to enable real-time monitoring, simulation, and optimization of network performance. When in…
INSIGHT: Enhancing Autonomous Driving Safety through Vision-Language Models on Context-Aware Hazard Detection and Edge Case Evaluation
Dianwei Chen, Zifan Zhang, Lei Cheng +2
Autonomous driving systems face significant challenges in handling unpredictable edge-case scenarios, such as adversarial pedestrian movements, dangerous vehicle maneuvers, and sud…
Poisoning Attacks and Defenses to Federated Unlearning
Wenbin Wang, Qiwen Ma, Zifan Zhang +3
Federated learning allows multiple clients to collaboratively train a global model with the assistance of a server. However, its distributed nature makes it susceptible to poisonin…
Byzantine-Robust Decentralized Federated Learning
Minghong Fang, Zifan Zhang, Hairi +5
Federated learning (FL) enables multiple clients to collaboratively train machine learning models without revealing their private training data. In conventional FL, the system foll…