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. 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.
66cc7f5a251afec29b909fa6
Chamith Mawela
66cc7f5a251afec29b909fa7
Chaouki Ben Issaid
66cc7f5a251afec29b909fa8
Mehdi Bennis
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. 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.
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.
federated learning FL federated averaging FedAvg model compression scheduling algorithms intent-based automation Language Model LLM neural architecture search NAS hyperparameter optimization HPO