基于LLM自动化的联邦学习的网络解决方案

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

August 23, 2024

作者: Chamith Mawela, Chaouki Ben Issaid, Mehdi Bennis

cs.AI

摘要

联邦学习（FL）为跨分布式设备的协作机器学习提供了一种有前景的方法。然而，其采用受到建立可靠通信架构的复杂性和对机器学习和网络编程专业知识的需求的阻碍。本文提出了一个全面的解决方案，简化了FL任务的编排，同时整合了基于意图的自动化。我们开发了一个用户友好的Web应用程序，支持联邦平均（FedAvg）算法，使用户能够通过直观的界面配置参数。后端解决方案有效地管理参数服务器和边缘节点之间的通信。我们还实现了模型压缩和调度算法，以优化FL的性能。此外，我们利用在定制数据集上训练的经过微调的语言模型（LLM）探索了FL中基于意图的自动化，使用户能够使用高级提示进行FL任务。我们观察到，基于LLM的自动化解决方案在减少了最多64%的传输字节和最多46%的CPU时间的同时，实现了与标准基于Web的解决方案相当的测试准确性。此外，我们利用LLM进行神经架构搜索（NAS）和超参数优化（HPO）以提高性能。我们观察到，通过使用这种方法，FL任务的测试准确性可以提高10-20%。

English

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.