Summary of Synergizing Foundation Models and Federated Learning: a Survey, by Shenghui Li et al.
Synergizing Foundation Models and Federated Learning: A Survey
by Shenghui Li, Fanghua Ye, Meng Fang, Jiaxu Zhao, Yun-Hin Chan, Edith C.-H. Ngai, Thiemo Voigt
First submitted to arxiv on: 18 Jun 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 This paper explores the intersection of Foundation Models (FMs) and Federated Learning (FL), highlighting the potential for customizing FMs to domain-specific tasks while preserving privacy. FMs, such as large language models, vision transformers, and multimodal models, require high-volume data during pre-training, which can be challenging for proprietary datasets. FL offers a solution by allowing distributed datasets from different participants while maintaining privacy. The paper discusses the strengths and limitations of combining FMs and FL, summarizes core techniques, future directions, and applications. Key concepts include Foundation Models, Federated Learning, multimodal models, vision transformers, large language models, pre-training, proprietary data, and privacy concerns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to make AI models work better for different tasks while keeping people’s private information safe. These AI models are called Foundation Models (FMs), and they need a lot of data to learn. The problem is that some data might be private, so we can’t just share it with anyone. Federated Learning (FL) is a way to solve this problem by working together with different groups of people who have their own datasets. This paper looks at how FMs and FL can work together to make AI models better for specific tasks while keeping private information safe. |
Keywords
» Artificial intelligence » Federated learning
