Summary of Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare, by Xingyu Li et al.
Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare
by Xingyu Li, Lu Peng, Yuping Wang, Weihua Zhang
First submitted to arxiv on: 10 May 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 Foundation models, specifically ChatGPT, LLaMa, and CLIP, have revolutionized artificial intelligence by integrating with federated learning (FL) to transform biomedical research. These foundation models, trained on vast datasets through methods like unsupervised pretraining, self-supervised learning, instructed fine-tuning, and reinforcement learning from human feedback, exhibit remarkable capabilities in generating coherent text and realistic images. Their application in biomedical settings, processing diverse data forms such as clinical reports, diagnostic images, and multimodal patient interactions, demonstrates their crucial role in advancing research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Foundation models are changing the way we do artificial intelligence. They’re like super smart computers that can understand and generate human-like text and images. This paper looks at how these models can be used with something called federated learning to make biomedical research better. Biomedical researchers use all kinds of data, like medical reports and pictures, to help them make new discoveries. Foundation models can help process this data and find important patterns that humans might miss. |
Keywords
» Artificial intelligence » Federated learning » Fine tuning » Llama » Pretraining » Reinforcement learning from human feedback » Self supervised » Unsupervised
