Summary of Research on Key Technologies For Cross-cloud Federated Training Of Large Language Models, by Haowei Yang et al.
Research on Key Technologies for Cross-Cloud Federated Training of Large Language Models
by Haowei Yang, Mingxiu Sui, Shaobo Liu, Xinyue Qian, Zhaoyang Zhang, Bingying Liu
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 proposes a novel approach to large language model training using cross-cloud federated training. By harnessing the computational resources of multiple clouds, this method enables efficient and secure training of massive models, addressing the limitations of single-cloud platforms. The study delves into key technologies such as data partitioning, communication optimization, and model aggregation algorithms, exploring their role in facilitating heterogeneous cloud platform compatibility. Additionally, it discusses data security and privacy protection strategies, including encryption and differential privacy techniques, ensuring the integrity of training processes. Experimental results demonstrate improved training efficiency, enhanced data security, and reduced costs, opening up broad application prospects for cross-cloud federated training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cross-cloud federated training is a new way to train large language models using many different clouds working together. This helps solve the problem of needing lots of computing power and data storage space. The paper looks at how this works, including how data gets divided up and shared between clouds, how information is sent efficiently, and how model results are combined. It also talks about making sure the training process stays private and secure. By doing experiments, the researchers showed that this method can be faster, more secure, and cheaper than traditional ways of training models. |
Keywords
» Artificial intelligence » Large language model » Optimization
