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…