title: A Web-Based Solution for Federated Learning with LLM-Based Automation
summary: 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.
paper_authors._id: 66cc7f5a251afec29b909fa6
paper_authors.name: Chamith Mawela
paper_authors._id: 66cc7f5a251afec29b909fa7
paper_authors.name: Chaouki Ben Issaid
paper_authors._id: 66cc7f5a251afec29b909fa8
paper_authors.name: Mehdi Bennis
paper_title: A Web-Based Solution for Federated Learning with LLM-Based Automation
paper_summary: 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.
paper_ai_summary: A web application with intent-based automation using a fine-tuned language model streamlines federated learning, optimizing performance and reducing resource usage while achieving high test accuracy.
paper_ai_keywords: federated learning FL federated averaging FedAvg model compression scheduling algorithms intent-based automation Language Model LLM neural architecture search NAS hyperparameter optimization HPO