Summary of Flora: Enhancing Vision-language Models with Parameter-efficient Federated Learning, by Duy Phuong Nguyen et al.
FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated Learning
by Duy Phuong Nguyen, J. Pablo Munoz, Ali Jannesari
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers develop a novel approach to train visual-language models (VLMs) that integrates vision and language while preserving data privacy and efficiency. The proposed method leverages Federated Learning and Low-Rank Adaptation (LoRA) to train VLMs across decentralized data sources, ensuring model adaptability and reducing training time by up to 34.72 times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach a computer to understand pictures and what they mean. This is called visual-language learning, and it’s really important for things like image captioning or searching for images online. The problem is that most computers need a lot of data to learn this skill, but sometimes that data can be private or hard to access. To solve this problem, scientists came up with a new way to train computers using something called Federated Learning and Low-Rank Adaptation (LoRA). This method lets computers learn from many different sources without needing all the data in one place, which makes it faster and more efficient. |
Keywords
» Artificial intelligence » Federated learning » Image captioning » Lora » Low rank adaptation
