Summary of A Web-based Solution For Federated Learning with Llm-based Automation, by Chamith Mawela and Chaouki Ben Issaid and Mehdi Bennis
A Web-Based Solution for Federated Learning with LLM-Based Automation
by Chamith Mawela, Chaouki Ben Issaid, Mehdi Bennis
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a comprehensive solution for Federated Learning (FL), a collaborative machine learning approach across distributed devices. The authors develop a user-friendly web application that integrates intent-based automation, simplifying the orchestration of FL tasks. The backend solution efficiently manages communication between parameter servers and edge nodes. To optimize performance, the authors implement model compression and scheduling algorithms. They also explore intent-based automation in FL using a fine-tuned Language Model (LLM), allowing users to conduct FL tasks using high-level prompts. 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%. Additionally, the authors leverage NAS and HPO using LLM to improve performance, achieving a 10-20% increase in test accuracy for carried-out FL tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier for people to work together on machine learning projects using something called Federated Learning. They built an easy-to-use website that lets you set up and run these projects without needing to know a lot of technical details. The website uses a special kind of computer program, called a Language Model, to help you get started. This makes it faster and more efficient than doing it the old way. The authors also found ways to make the process better by using other techniques, like finding the best combination of settings for your project. |
Keywords
» Artificial intelligence » Federated learning » Language model » Machine learning » Model compression
