Summary of Fedspallm: Federated Pruning Of Large Language Models, by Guangji Bai et al.
FedSpaLLM: Federated Pruning of Large Language Models
by Guangji Bai, Yijiang Li, Zilinghan Li, Liang Zhao, Kibaek Kim
First submitted to arxiv on: 18 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 FedSpaLLM is a novel framework for pruning Large Language Models (LLMs) in privacy-preserving settings. Traditional pruning methods rely on public access to calibration data, which is impractical for applications requiring data confidentiality. To address this challenge, FedSpaLLM enables clients to prune their models locally using private data while accounting for system heterogeneity and maintaining communication efficiency. The framework introduces three key innovations: (1) an _0-norm aggregation function that preserves important model parameters; (2) an adaptive mask expansion technique that meets global sparsity targets; and (3) a layer sampling strategy that reduces communication overhead and personalizes the pruning process. Extensive experiments demonstrate the effectiveness of FedSpaLLM in diverse federated settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a powerful language model that can understand and generate human-like text, but it’s too big to fit on your computer or phone. Pruning is a way to make the model smaller without losing its abilities. The problem is that most pruning methods require access to special data that might not be available in some situations. FedSpaLLM is a new solution that lets clients prune their models privately, while also considering differences between devices and reducing communication overhead. |
Keywords
» Artificial intelligence » Language model » Mask » Pruning
