Summary of Save It All: Enabling Full Parameter Tuning For Federated Large Language Models Via Cycle Block Gradient Descent, by Lin Wang et al.
Save It All: Enabling Full Parameter Tuning for Federated Large Language Models via Cycle Block Gradient Descent
by Lin Wang, Zhichao Wang, Xiaoying Tang
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 introduces a novel approach to efficiently train and fine-tune large language models (LLMs) in federated learning (FL) frameworks. The proposed method, FedCyBGD, uses Cycle Block Gradient Descent to periodically update the model, reducing computational and memory resource demands. A compression scheme is designed to further decrease model download costs by only updating and uploading selected blocks of parameters. This approach achieves state-of-the-art performance for FL LLM training while significantly reducing associated costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make large language models work better in a way that involves lots of devices sharing information. Right now, this process is slow and uses too much memory. The new method, called FedCyBGD, makes it faster and more efficient by only sending small pieces of the model between devices. This means less data needs to be shared, which saves time and resources. It also works really well, achieving the best results so far for training these models. |
Keywords
* Artificial intelligence * Federated learning * Gradient descent
