Summary of Federated Large Language Models: Current Progress and Future Directions, by Yuhang Yao et al.
Federated Large Language Models: Current Progress and Future Directions
by Yuhang Yao, Jianyi Zhang, Junda Wu, Chengkai Huang, Yu Xia, Tong Yu, Ruiyi Zhang, Sungchul Kim, Ryan Rossi, Ang Li, Lina Yao, Julian McAuley, Yiran Chen, Carlee Joe-Wong
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 Large language models (LLMs) have become increasingly popular in real-world applications, but their training relies heavily on high-quality data. Federated learning offers a solution by enabling multiple clients to collaborate without sharing local data. However, this approach introduces new challenges, such as model convergence issues due to heterogeneous data and high communication costs. This paper surveys recent advances and future directions for federated LLMs (FedLLM), focusing on fine-tuning and prompt learning in a federated setting. The study discusses existing work, associated research challenges, and proposes potential research directions, including pre-training and enhancing federated learning with LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are used in many real-world applications, but collecting data for these models can be private concerns. Federated learning is a solution that allows multiple clients to work together without sharing their local data. However, this approach has its own challenges, such as making sure the model works well with different types of data and high communication costs. This paper looks at recent advances and future directions for using federated learning with large language models. It focuses on how to fine-tune these models and make them better in a collaborative setting. |
Keywords
» Artificial intelligence » Federated learning » Fine tuning » Prompt
