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

TL;DR

This paper introduces a web-based federated learning platform enhanced with LLM-driven automation, simplifying setup, improving efficiency, and boosting performance through advanced optimization techniques.

Contribution

It presents a user-friendly web application for federated learning that integrates intent-based automation using LLMs and employs NAS and HPO for performance enhancement.

Findings

Achieves comparable accuracy to standard solutions with reduced data transfer and CPU usage.

LLM-based automation reduces resource consumption significantly.

Performance improvements of 10-20% in test accuracy through NAS and HPO.

Abstract

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. We develop a user-friendly web application 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. We also implement model compression and scheduling algorithms to optimize FL performance. Furthermore, we explore intent-based automation in FL using a fine-tuned Language Model (LLM) trained on a tailored dataset, allowing users to conduct FL tasks using high-level prompts. We observe 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. Also, we leverage the neural architecture search (NAS) and hyperparameter optimization (HPO) using LLM to improve the performance. We observe that by using this approach test accuracy can be improved by 10-20% for the carried out FL tasks.