4 citations · 7 across the 17 of their papers we have counts for
18 papers
Network-in-the-Loop at Scale: GPU-Batched 5G Simulation for Massively Parallel Robot Learning
Zifan Zhang, Mingzhe Han, Kannan Athreya +1
Massively parallel GPU simulators train multi-robot policies in thousands of environments, and many fleets use private Fifth-Generation (5G) networks, where each robot's delay depe…
CENTILE: A Telemetry Foundation Model Evaluated by the Decisions It Drives
Zifan Zhang, Zhichao Hou, Tingxiang Ji +1
Modern computing and networking infrastructure emits telemetry continuously, yet operators convert it into decisions with a separate predictor per task, entity, and horizon. One ge…
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…
OpenTwin: Closed-Loop Digital Twins for Trustworthy Policy Deployment in Open RAN
Zifan Zhang, Md Sharif Hossen, Dara Ron +2
In open radio access networks (O-RAN), the near-real-time RAN Intelligent Controller (RIC) hosts third-party xApps whose training and validation risk disrupting the operational net…
Network Digital Untwinning: Towards Backward Optimization of Digital Twins
Zifan Zhang, Dianwei Chen, Anjun Gao +5
Network digital twins (NDTs) are transforming network management by offering precise virtual replicas of physical network systems. However, their reliance on diverse and sensitive…
On Transferring, Merging, and Splitting Task-Oriented Network Digital Twins
Zifan Zhang, Minghong Fang, Mingzhe Chen +1
The integration of digital twinning technologies is driving next-generation networks toward new capabilities, allowing operators to thoroughly understand network conditions, effici…
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…
Digital Network Twins for Next-generation Wireless: Creation, Optimization, and Challenges
Zifan Zhang, Zhiyuan Peng, Hanzhi Yu +2
Digital network twins (DNTs), by representing a physical network using a virtual model, offer significant benefits such as streamlined network development, enhanced productivity, a…
On the Hardness of Decentralized Multi-Agent Policy Evaluation under Byzantine Attacks
Hairi, Minghong Fang, Zifan Zhang +2
In this paper, we study a fully-decentralized multi-agent policy evaluation problem, which is an important sub-problem in cooperative multi-agent reinforcement learning, in the pre…
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…
Securing Distributed Network Digital Twin Systems Against Model Poisoning Attacks
Zifan Zhang, Minghong Fang, Mingzhe Chen +3
In the era of 5G and beyond, the increasing complexity of wireless networks necessitates innovative frameworks for efficient management and deployment. Digital twins (DTs), embodyi…
Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks
Zifan Zhang, Yuchen Liu, Zhiyuan Peng +3
Optimizing edge caching is crucial for the advancement of next-generation (nextG) wireless networks, ensuring high-speed and low-latency services for mobile users. Existing data-dr…
Mapping Wireless Networks into Digital Reality through Joint Vertical and Horizontal Learning
Zifan Zhang, Mingzhe Chen, Zhaohui Yang +1
In recent years, the complexity of 5G and beyond wireless networks has escalated, prompting a need for innovative frameworks to facilitate flexible management and efficient deploym…
Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction
Zifan Zhang, Minghong Fang, Jiayuan Huang +1
Federated Learning (FL) offers a distributed framework to train a global control model across multiple base stations without compromising the privacy of their local network data. T…
Sample and Communication Efficient Fully Decentralized MARL Policy Evaluation via a New Approach: Local TD update
Fnu Hairi, Zifan Zhang, Jia Liu
In actor-critic framework for fully decentralized multi-agent reinforcement learning (MARL), one of the key components is the MARL policy evaluation (PE) problem, where a set of $N…