Summary of Federating to Grow Transformers with Constrained Resources Without Model Sharing, by Shikun Shen et al.
Federating to Grow Transformers with Constrained Resources without Model Sharing
by Shikun Shen, Yifei Zou, Yuan Yuan, Yanwei Zheng, Peng Li, Xiuzhen Cheng, Dongxiao Yu
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The paper proposes Fed-Grow, a federated framework for multiple participants to cooperatively scale a transformer from their pre-trained small models. The Dual-LiGO architecture is designed to address heterogeneity issues and share implicit knowledge among participants. Unlike traditional model-sharing approaches, Fed-Grow only shares the Global-LiGO, ensuring user privacy protection. Experimental results demonstrate higher accuracy, better precision, and lower resource consumption compared to state-of-the-art methods. This decentralized approach enables large-scale transformers to be extended to distributed scenarios, encouraging more users to adopt these models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make powerful transformers accessible to everyone, not just those with lots of computing resources. It’s like a team effort where people share their small transformer models and work together to grow them into bigger, better models. This way, people can still use the benefits of large-scale transformers even if they don’t have a lot of resources. |
Keywords
» Artificial intelligence » Precision » Transformer
