Summary of Robust Federated Finetuning Of Foundation Models Via Alternating Minimization Of Lora, by Shuangyi Chen et al.
Robust Federated Finetuning of Foundation Models via Alternating Minimization of LoRA
by Shuangyi Chen, Yue Ju, Hardik Dalal, Zhongwen Zhu, Ashish Khisti
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 Parameter-Efficient Fine-Tuning (PEFT) is a technique that updates only specific model parameters, reducing computational and memory demands. PEFT also decreases data transfer in federated learning settings. This work integrates LoRA, a well-known PEFT method, with federated fine-tuning, introducing RoLoRA, an alternating minimization approach for LoRA, enhancing robustness against decreasing fine-tuning parameters and increasing data heterogeneity. The results show that RoLoRA not only benefits from reduced communication but also improves robustness and effectiveness in various federated fine-tuning scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to train models called Parameter-Efficient Fine-Tuning (PEFT). It helps computers use less power and memory by only updating certain parts of the model. PEFT also makes it easier to share data in situations where many devices need to work together. The authors combined this method with another technique called LoRA, which is a type of fine-tuning that works well for big models. They made it better by adding an extra step and calling it RoLoRA. This new approach helps models be more robust and effective when working together. |
Keywords
» Artificial intelligence » Federated learning » Fine tuning » Lora » Parameter efficient
