Summary of Empowering Federated Learning For Massive Models with Nvidia Flare, by Holger R. Roth et al.
Empowering Federated Learning for Massive Models with NVIDIA FLARE
by Holger R. Roth, Ziyue Xu, Yuan-Ting Hsieh, Adithya Renduchintala, Isaac Yang, Zhihong Zhang, Yuhong Wen, Sean Yang, Kevin Lu, Kristopher Kersten, Camir Ricketts, Daguang Xu, Chester Chen, Yan Cheng, Andrew Feng
First submitted to arxiv on: 12 Feb 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 The abstract proposes a solution to the challenge of handling data effectively in large language models (LLMs) by utilizing federated learning enabled by NVIDIA FLARE. The method allows for easy and scalable integration, enabling parameter-efficient and full supervised fine-tuning of LLMs for natural language processing and biopharmaceutical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a super powerful computer that can learn from lots of different sources without needing all the data in one place. This paper talks about how to make this happen using something called federated learning, which helps big language models get better at understanding language and working with biomedicine. It’s like a special way to share information between computers without sharing too much, keeping everything private and secure. |
Keywords
* Artificial intelligence * Federated learning * Fine tuning * Natural language processing * Parameter efficient * Supervised
