The prosperity of deep neural networks (DNNs) is largely benefited from open-source
datasets, based on which users can evaluate and improve their methods. In this paper, we …

The rapid development of artificial intelligence (AI) has brought about revolutionary changes
to industrial wireless networks. Meanwhile, these AI models have also incurred a more …

Federated Learning (FL) is a privacy-preserving distributed learning approach that is rapidly
developing in an era where privacy protection is increasingly valued. It is this rapid …

Recent advances in multi-agent reinforcement learning (MARL) have opened up vast
application prospects, including swarm control of drones, collaborative manipulation by …

This paper investigates the threat of backdoors in Deep Reinforcement Learning (DRL)
agent policies and proposes a novel method for their detection at runtime. Our study focuses …

Establishing robust policies is essential to counter attacks or disturbances affecting deep
reinforcement learning (DRL) agents. Recent studies explore state-adversarial robustness …

Large Language Models (LLMs) have shown significant promise in decision-making tasks
when fine-tuned on specific applications, leveraging their inherent common sense and …

Currently, sample-specific backdoor attacks (SSBAs) are the most advanced and malicious
methods since they can easily circumvent most of the current backdoor defenses. In this …

While the advent of Deep Reinforcement Learning (DRL) has substantially improved the
efficiency of Autonomous Vehicles (AVs), it makes them vulnerable to backdoor attacks that …