Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning

The introduction of the Tool-Aided Evolutionary Large Language Model (T-ELLM) framework marks a significant advancement in Federated Learning (FL), which allows for distributed model training across edge devices while prioritizing user privacy. Traditional methods of device selection and resource allocation in FL are often cumbersome, requiring domain-specific knowledge and extensive hyperparameter tuning. T-ELLM addresses these challenges by mathematically decoupling the optimization problem, thus enabling more efficient learning of device selection policies. By leveraging natural language prompts, T-ELLM enhances adaptability across various network conditions, reducing reliance on real-world interactions and minimizing communication overhead. This innovative approach not only streamlines the training process but also ensures high-fidelity decision-making, making it a promising solution for the dynamic and heterogeneous nature of wireless environments. The theoretical analysis support…