Collaborative Communication for Edge LLM Servicing in Adversarial Networks: An MARL-Empowered Stackelberg Game Approach

With the rapid expansion of IoT and the advent of the 6G era, ensuring efficient and secure communication for the edge large language model (LLM) servicing has become a critical priority. However, malicious relay nodes injecting delays pose a significant threat to network performance and security. This article proposes a novel framework combining Stackelberg Game theory with multiagent reinforcement learning (MARL) to mitigate communication delay injection attacks in edge networks. By modeling the interaction between edge devices and malicious nodes as a Stackelberg Game, where edge devices act as followers optimizing relay strategies and malicious nodes as leaders seeking disruption, our approach enables edge devices to cooperatively identify and avoid malicious nodes. The distributed MARL framework allows each edge device to learn their optimal strategies independently, enhancing network resilience. Experimental results demonstrate the effectiveness of our scheme in effectively reducing the impact of malicious nodes and improving the deployment capability of edge computing services.