Summary of Lanfl: Differentially Private Federated Learning with Large Language Models Using Synthetic Samples, by Huiyu Wu et al.
LanFL: Differentially Private Federated Learning with Large Language Models using Synthetic Samples
by Huiyu Wu, Diego Klabjan
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 introduces a novel Federated Learning (FL) scheme for Large Language Models (LLMs), named LanFL, which is designed to address the challenges posed by the massive scale and complexity of LLMs. The traditional FL methods are impractical due to high computational and communication costs when applied to LLMs with tens to hundreds of billions of parameters. To overcome this limitation, LanFL treats the underlying LLMs as black boxes, relying on prompt-based training and a synthetic sample generation mechanism to facilitate knowledge sharing among participants while preserving differential privacy. The authors demonstrate the effectiveness of LanFL through extensive experiments across various tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LanFL is a new way for people to work together to train language models without sharing their private data. This is important because these models can be very big and complex, making it hard for traditional methods to work efficiently. LanFL lets participants share knowledge using prompts and synthetic samples, while keeping their own data private. The authors tested LanFL and showed that it works well across different tasks. |
Keywords
» Artificial intelligence » Federated learning » Prompt
