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Article overview
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FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning | | Weirui Kuang
; Bingchen Qian
; Zitao Li
; Daoyuan Chen
; Dawei Gao
; Xuchen Pan
; Yuexiang Xie
; Yaliang Li
; Bolin Ding
; Jingren Zhou
; | | Date: |
1 Sep 2023 | | Abstract: | LLMs have demonstrated great capabilities in various NLP tasks. Different
entities can further improve the performance of those LLMs on their specific
downstream tasks by fine-tuning LLMs. When several entities have similar
interested tasks, but their data cannot be shared because of privacy concerns
regulations, federated learning (FL) is a mainstream solution to leverage the
data of different entities. However, fine-tuning LLMs in federated learning
settings still lacks adequate support from existing FL frameworks because it
has to deal with optimizing the consumption of significant communication and
computational resources, data preparation for different tasks, and distinct
information protection demands. This paper first discusses these challenges of
federated fine-tuning LLMs, and introduces our package FS-LLM as a main
contribution, which consists of the following components: (1) we build an
end-to-end benchmarking pipeline, automizing the processes of dataset
preprocessing, federated fine-tuning execution, and performance evaluation on
federated LLM fine-tuning; (2) we provide comprehensive federated
parameter-efficient fine-tuning algorithm implementations and versatile
programming interfaces for future extension in FL scenarios with low
communication and computation costs, even without accessing the full model; (3)
we adopt several accelerating and resource-efficient operators for fine-tuning
LLMs with limited resources and the flexible pluggable sub-routines for
interdisciplinary study. We conduct extensive experiments to validate the
effectiveness of FS-LLM and benchmark advanced LLMs with state-of-the-art
parameter-efficient fine-tuning algorithms in FL settings, which also yields
valuable insights into federated fine-tuning LLMs for the research community.
To facilitate further research and adoption, we release FS-LLM at
this https URL. | | Source: | arXiv, 2309.00363 | | Services: | Forum | Review | PDF | Favorites | |
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