- The paper introduces a web-based platform for federated learning that leverages a fine-tuned LLM to automate and simplify task configuration.
- It adapts the FedAvg algorithm with model compression and scheduling, reducing communication overhead by up to 64% and CPU time by 46%.
- The study further integrates NAS and HPO to boost test accuracy by 10-20% on datasets like CIFAR-10 while managing increased computational overhead.
A Web-Based Solution for Federated Learning with LLM-Based Automation
Introduction
The paper addresses the persistent challenges in implementing federated learning (FL) due to technical complexities in setting up reliable communication infrastructures and the need for expertise in both ML and network programming. The authors propose an integrated web-based solution to streamline the orchestration of FL tasks, incorporating intent-based automation using a fine-tuned LLM.
Simplifying Federated Learning
Federated Learning (FL) has gained prominence as a decentralized paradigm that allows training models collaboratively across distributed devices without sharing raw data, thereby preserving user privacy. Traditional methods of implementing FL necessitate sophisticated knowledge in ML and network programming to handle client-server communications, which poses a significant barrier to wide-scale adoption. Existing frameworks such as Flower, PySyft, and TensorFlow Federated provide some solutions but still require considerable programming expertise and lack direct support for FL optimization tasks such as model compression and scheduling.
Proposed Web Solution
The authors present a user-friendly web application designed to streamline FL implementations. This solution comprises:
- Front-end Interface: Developed using React, enabling users to submit FL tasks through an intuitive web form, specifying parameters like mini-batch size, epochs, learning rate, and others as detailed in the web form fields.
- Backend Communication: Utilizing WebSockets for efficient bi-directional communication between the parameter server and multiple clients, ensuring real-time data exchange and reduced overhead.
- Modified Federated Averaging Algorithm: Adapting the FedAvg algorithm to include model compression schemes (quantization and sparsification) and scheduling mechanisms (random, round-robin, latency-proportional, full) to optimize the performance of the FL process.
Intent-Based Automation with LLM
Recent advancements in LLMs and their applications in various downstream tasks inspired the authors to integrate LLM-based automation within the FL framework. The process involves:
- Fine-Tuning LLM: Using a newly constructed dataset of intent and corresponding JSON configuration pairs to fine-tune a Mistral-7B model with the QLoRA method. The dataset incorporates variations in intent phrasing to cover a broad range of possible user inputs.
- Model Configuration and Task Automation: Upon receiving a user intent, the fine-tuned LLM generates a JSON configuration file, which is sent to the parameter server. The server then requests a model architecture from the OpenAI ChatGPT API, based on dataset properties provided by the clients.
Experimentation and Results
The efficacy of the proposed web-based FL solution and its automated counterpart (LLM-FedAvg) was evaluated through several simulation scenarios using datasets such as MNIST, CIFAR-10, Fashion-MNIST, and SVHN. The experimental setup involved clients running on Raspberry Pi devices and a parameter server on a laptop. The results demonstrated that:
- Communication Overhead and CPU Time: LLM-FedAvg showed a reduction in communication overhead by up to 64% and CPU time by up to 46% compared to the traditional web-based solution.
- Test Accuracy: Both methods achieved comparable test accuracy, with LLM-FedAvg occasionally surpassing the web-based approach due to more efficient model suggestions from ChatGPT.
Enhancing Performance through NAS and HPO
To further improve the performance of the LLM-FedAvg method, the authors incorporated Neural Architecture Search (NAS) and Hyperparameter Optimization (HPO). They leveraged the LLM to generate a diverse search space of model architectures and employed a selective halving approach to optimize hyperparameters. Results from NAS and HPO (LLM-FedAvgNAS) indicated a substantial increase in test accuracy for challenging datasets:
- Test Accuracy Improvement: For datasets like CIFAR-10, Fashion-MNIST, and SVHN, LLM-FedAvgNAS achieved higher test accuracy, showing improvements in the range of 10-20%.
- Computational Overhead: The enhanced accuracy from LLM-FedAvgNAS came at the cost of increased computational footprint during the offline search process, necessitating higher computational resources.
Conclusion and Future Directions
This research provides a robust and scalable solution to lower the barrier of entry for federated learning. By introducing a web-based platform integrated with LLM-based automation and further enhanced by NAS and HPO, this work provides a comprehensive approach to both streamline and optimize FL tasks. Future developments could explore reducing the computational overhead of NAS and HPO by leveraging more efficient algorithms and distributed computing resources at a larger scale, potentially making advanced FL accessible to an even broader audience.