Federated Large Language Models for Smart Grid: A Communication Efficient LoRA Approach

The increasing complexity of modern power systems and the proliferation of data-driven applications in smart grids demand advanced machine learning models for enhanced efficiency, reliability, and sustainability. However, deploying large language models (LLMs) in smart grids faces challenges such as high communication overhead, limited bandwidth, and data privacy concerns. Federated learning (FL) has emerged as a solution by enabling decentralized model training across distributed devices without sharing raw data, thereby preserving privacy. Yet, the size of LLMs imposes significant communication constraints in FL environments, especially in bandwidth-limited smart grid networks. This paper proposes a novel approach leveraging *Low-Rank Adaptation* (LoRA) to enhance the communication efficiency of LLMs in federated learning for smart grid applications. By injecting low-rank matrices into LLM layers, LoRA significantly reduces the number of model parameters updated during training, thereby minimizing the communication overhead while maintaining model performance. We present a comprehensive analysis of this approach, focusing on its impact on model accuracy, communication cost, and scalability in distributed smart grid environments. Our findings demonstrate that LoRA-enhanced federated LLMs provide an effective balance between communication efficiency, privacy preservation, and performance, offering a promising solution for future AI-driven smart grids.