4 citations · 6 across the 12 of their papers we have counts for
12 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…
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…
Byzantine-Robust Federated Learning over Ring-All-Reduce Distributed Computing
Minghong Fang, Zhuqing Liu, Xuecen Zhao +1
Federated learning (FL) has gained attention as a distributed learning paradigm for its data privacy benefits and accelerated convergence through parallel computation. Traditional…
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…
Do We Really Need to Design New Byzantine-robust Aggregation Rules?
Minghong Fang, Seyedsina Nabavirazavi, Zhuqing Liu +3
Federated learning (FL) allows multiple clients to collaboratively train a global machine learning model through a server, without exchanging their private training data. However,…
Adversarial Attacks to Multi-Modal Models
Zhihao Dou, Xin Hu, Haibo Yang +2
Multi-modal models have gained significant attention due to their powerful capabilities. These models effectively align embeddings across diverse data modalities, showcasing superi…
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…
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…