2 citations · 4 across the 10 of their papers we have counts for
7 papers · 1 filter
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…
OpticGAI: Generative AI-aided Deep Reinforcement Learning for Optical Networks Optimization
Siyuan Li, Xi Lin, Yaju Liu +2
Deep Reinforcement Learning (DRL) is regarded as a promising tool for optical network optimization. However, the flexibility and efficiency of current DRL-based solutions for optic…
Towards Multi-Task Generative-AI Edge Services with an Attention-based Diffusion DRL Approach
Yaju Liu, Xi Lin, Siyuan Li +3
As an emerging paradigm of content creation, AI-Generated Content (AIGC) has been widely adopted by a large number of edge end users. However, the requests for generated content fr…
Multi-Agent RL-Based Industrial AIGC Service Offloading over Wireless Edge Networks
Siyuan Li, Xi Lin, Hansong Xu +4
Currently, the generative model has garnered considerable attention due to its application in addressing the challenge of scarcity of abnormal samples in the industrial Internet of…
Trustworthy AI-Generative Content for Intelligent Network Service: Robustness, Security, and Fairness
Siyuan Li, Xi Lin, Yaju Liu +2
AI-generated content (AIGC) models, represented by large language models (LLM), have revolutionized content creation. High-speed next-generation communication technology is an idea…
Spikewhisper: Temporal Spike Backdoor Attacks on Federated Neuromorphic Learning over Low-power Devices
Hanqing Fu, Gaolei Li, Jun Wu +4
Federated neuromorphic learning (FedNL) leverages event-driven spiking neural networks and federated learning frameworks to effectively execute intelligent analysis tasks over amou…
What Makes Good Collaborative Views? Contrastive Mutual Information Maximization for Multi-Agent Perception
Wanfang Su, Lixing Chen, Yang Bai +4
Multi-agent perception (MAP) allows autonomous systems to understand complex environments by interpreting data from multiple sources. This paper investigates intermediate collabora…