Summary of Personalized Federated Fine-tuning For Llms Via Data-driven Heterogeneous Model Architectures, by Yicheng Zhang et al.
Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures
by Yicheng Zhang, Zhen Qin, Zhaomin Wu, Jian Hou, Shuiguang Deng
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel federated learning framework, dubbed FedAMoLE, is proposed to fine-tune large language models (LLMs) without sharing sensitive instruction data. This framework addresses the limitations of uniform model architectures by enabling personalized heterogeneous model architectures tailored to varying data distributions. The adaptive mixture of LoRA experts (MoLE) module aggregates heterogeneous models, while a reverse selection-based expert assignment strategy optimizes model architectures for optimal performance. Experimental results demonstrate an average accuracy improvement of 5.14% compared to existing approaches, showcasing the framework’s effectiveness and scalability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning helps us fine-tune language models without sharing sensitive data. This is important because large amounts of instruction data are needed to make sure language models understand human instructions correctly. The problem is that this data might contain private information, so we can’t just share it openly. To solve this issue, scientists have developed a new way to fine-tune language models called FedAMoLE. This approach creates unique models for different types of data and adjusts them based on the amount and format of the data. By doing so, FedAMoLE improves accuracy by an average of 5.14% compared to previous methods while keeping the data private. |
Keywords
» Artificial intelligence » Federated learning » Lora
