Summary of Exploring Gradient Subspaces: Addressing and Overcoming Lora’s Limitations in Federated Fine-tuning Of Large Language Models, by Navyansh Mahla et al.
Exploring Gradient Subspaces: Addressing and Overcoming LoRA’s Limitations in Federated Fine-Tuning of Large Language Models
by Navyansh Mahla, Kshitij Sharad Jadhav, Ganesh Ramakrishnan
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 investigates the limitations of popular Federated Learning (FL) frameworks that utilize Low-Rank Adaptation (LoRA) to fine-tune Large Language Models (LLMs). LoRA is a technique used in FL to adapt LLMs to specific downstream tasks without sharing private data. However, this approach has been found to be suboptimal due to its constrained subspace learning of low-rank matrices. In contrast, direct weight averaging outperforms LoRA-based strategies and leads to superior performance for fine-tuned models. The paper also evaluates the effectiveness of GaLore, a low-rank gradient-based optimizer used during local training steps, in combination with direct-weight aggregation. The findings suggest that this approach outperforms federated LoRA methods like FlexLoRA and FFA-LoRA across both text and image modalities. This research highlights the importance of reassessing the reliance on LoRA within FL contexts, paving the way for more efficient training methodologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make large language models work better in teams without sharing private data. It finds that a popular technique called Low-Rank Adaptation (LoRA) doesn’t work as well as it could because of its limitations. Instead, the authors suggest using a different approach called direct weight averaging, which works better and leads to better results. The paper also looks at another optimizer called GaLore and shows that combining it with direct-weight aggregation is even more effective. Overall, this research suggests that we should rethink our reliance on LoRA and look for new ways to make language models work better in teams. |
Keywords
» Artificial intelligence » Federated learning » Lora » Low rank adaptation
