A Fast, Performant, Secure Distributed Training Framework For Large Language Model Article Swipe
Wei Huang
,
Yinggui Wang
,
Anda Cheng
,
Aihui Zhou
,
Chaofan Yu
,
Lei Wang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.09796
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.09796
The distributed (federated) LLM is an important method for co-training the domain-specific LLM using siloed data. However, maliciously stealing model parameters and data from the server or client side has become an urgent problem to be solved. In this paper, we propose a secure distributed LLM based on model slicing. In this case, we deploy the Trusted Execution Environment (TEE) on both the client and server side, and put the fine-tuned structure (LoRA or embedding of P-tuning v2) into the TEE. Then, secure communication is executed in the TEE and general environments through lightweight encryption. In order to further reduce the equipment cost as well as increase the model performance and accuracy, we propose a split fine-tuning scheme. In particular, we split the LLM by layers and place the latter layers in a server-side TEE (the client does not need a TEE). We then combine the proposed Sparsification Parameter Fine-tuning (SPF) with the LoRA part to improve the accuracy of the downstream task. Numerous experiments have shown that our method guarantees accuracy while maintaining security.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2401.09796
- https://arxiv.org/pdf/2401.09796
- OA Status
- green
- Cited By
- 4
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391046568
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391046568Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2401.09796Digital Object Identifier
- Title
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A Fast, Performant, Secure Distributed Training Framework For Large Language ModelWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-01-18Full publication date if available
- Authors
-
Wei Huang, Yinggui Wang, Anda Cheng, Aihui Zhou, Chaofan Yu, Lei WangList of authors in order
- Landing page
-
https://arxiv.org/abs/2401.09796Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2401.09796Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2401.09796Direct OA link when available
- Concepts
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Computer science, Encryption, Scheme (mathematics), Embedding, Distributed computing, Domain (mathematical analysis), Slicing, Client-side, Server-side, Task (project management), Computer network, Artificial intelligence, Management, Mathematics, World Wide Web, Economics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.accuracy | 158, 171 |
| abstract_inverted_index.executed | 85 |
| abstract_inverted_index.increase | 106 |
| abstract_inverted_index.proposed | 146 |
| abstract_inverted_index.slicing. | 49 |
| abstract_inverted_index.stealing | 18 |
| abstract_inverted_index.Execution | 57 |
| abstract_inverted_index.Parameter | 148 |
| abstract_inverted_index.accuracy, | 111 |
| abstract_inverted_index.embedding | 74 |
| abstract_inverted_index.equipment | 101 |
| abstract_inverted_index.important | 6 |
| abstract_inverted_index.security. | 174 |
| abstract_inverted_index.structure | 71 |
| abstract_inverted_index.downstream | 161 |
| abstract_inverted_index.fine-tuned | 70 |
| abstract_inverted_index.guarantees | 170 |
| abstract_inverted_index.parameters | 20 |
| abstract_inverted_index.(federated) | 2 |
| abstract_inverted_index.Environment | 58 |
| abstract_inverted_index.Fine-tuning | 149 |
| abstract_inverted_index.co-training | 9 |
| abstract_inverted_index.distributed | 1, 44 |
| abstract_inverted_index.encryption. | 94 |
| abstract_inverted_index.experiments | 164 |
| abstract_inverted_index.fine-tuning | 116 |
| abstract_inverted_index.lightweight | 93 |
| abstract_inverted_index.maintaining | 173 |
| abstract_inverted_index.maliciously | 17 |
| abstract_inverted_index.particular, | 119 |
| abstract_inverted_index.performance | 109 |
| abstract_inverted_index.server-side | 133 |
| abstract_inverted_index.environments | 91 |
| abstract_inverted_index.communication | 83 |
| abstract_inverted_index.Sparsification | 147 |
| abstract_inverted_index.domain-specific | 11 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |