FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning-Reference-Cited by-同舟云学术

FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning

Published:2024-08-24 Issue: Volume:1 Page:5260-5271
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Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Kuang Weirui¹^ORCID,Qian Bingchen¹^ORCID,Li Zitao²^ORCID,Chen Daoyuan¹^ORCID,Gao Dawei³^ORCID,Pan Xuchen³^ORCID,Xie Yuexiang¹^ORCID,Li Yaliang¹^ORCID,Ding Bolin²^ORCID,Zhou Jingren²^ORCID

Affiliation:

1. Alibaba Group, Hangzhou, China

2. Alibaba Group, Bellevue, USA

3. Alibaba Group, Beijing, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671573

Reference70 articles.

1. Anthropic. 2023. Introducing Claude.

2. Jiamu Bai, Daoyuan Chen, Bingchen Qian, Liuyi Yao, and Yaliang Li. 2024. Federated Fine-tuning of Large Language Models under Heterogeneous Language Tasks and Client Resources. arXiv preprint arXiv:2402.11505 (2024).

3. Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, et al. 2019. Towards federated learning at scale: System design. In Proc. of machine learning and systems (MLSys'19), Vol. 1. 374--388.

4. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language Models are Few-Shot Learners. In Proc. of the Advances in Neural Information Processing Systems (NeurIPS'20), Vol. 33. 1877--1901.

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