FDI attack detection and LLM-assisted resource allocation for 6G edge intelligence-empowered distribution power grid

The intelligent operation management of distribution services is crucial for the stability of power systems. Integrating the large language model (LLM) with 6G edge intelligence provides customized management solutions. However, the adverse effects of false data injection (FDI) attacks on the performance of LLMs cannot be overlooked. Therefore, we propose an FDI attack detection and LLM-assisted resource allocation algorithm for 6G edge intelligence-empowered distribution power grids. First, we formulate a resource allocation optimization problem. The objective is to minimize the weighted sum of the global loss function and total LLM fine-tuning delay under constraints of long-term privacy entropy and energy consumption. Then, we decouple it based on virtual queues. We utilize an LLM-assisted deep Q network (DQN) to learn the resource allocation strategy and design an FDI attack detection mechanism to ensure that fine-tuning remains on the correct path. Simulations demonstrate that the proposed algorithm has excellent performance in convergence, delay, and security.