A Web-Based Solution for Federated Learning With LLM-Based Automation

Federated Learning (FL) offers a promising approach for collaborative machine learning across distributed devices. However, its adoption is hindered by the complexity of building reliable communication architectures and the need for expertise in both machine learning and network programming. This paper presents a comprehensive solution that simplifies the orchestration of FL tasks while integrating intent-based automation. A user-friendly web application is developed supporting the federated averaging (FedAvg) algorithm, enabling users to configure parameters through an intuitive interface. The backend solution efficiently manages communication between the parameter server and edge nodes. Model compression and scheduling algorithms are implemented to optimize FL performance. Additionally, intent-based automation in FL is explored using a fine-tuned Language Model (LLM) trained on a tailored dataset, enabling users to perform FL tasks through high-level prompts. It is shown that the LLM-based automated solution achieves comparable test accuracy to the standard web-based solution while reducing transferred bytes by up to 64% and CPU time by up to 46% for FL tasks. Furthermore, neural architecture search (NAS) and hyperparameter optimization (HPO) are leveraged using the LLM to enhance performance, resulting in a 10-20% improvement in test accuracy for the conducted FL tasks.