On the Convergence of Zeroth-Order Federated Tuning for Large Language Models-Reference-Cited by-同舟云学术

On the Convergence of Zeroth-Order Federated Tuning for Large Language Models

Published:2024-08-24 Issue: Volume:202 Page:1827-1838
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
language:
Short-container-title:

Author:

Ling Zhenqing¹^ORCID,Chen Daoyuan²^ORCID,Yao Liuyi²^ORCID,Li Yaliang³^ORCID,Shen Ying⁴^ORCID

Affiliation:

1. Sun Yat-sen University, Shenzhen, Guangdong, China

2. Alibaba Group, Hangzhou, Zhejiang, China

3. Alibaba Group, Bellevue, Washington, USA

4. Sun Yat-sen University & Pazhou Lab, Shenzhen, Guangdong, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671865

Reference65 articles.

1. Extended version of this paper. arXiv preprint arXiv:2402.05926.

2. Alekh Agarwal, Martin J Wainwright, Peter Bartlett, and Pradeep Ravikumar. 2009. Information-theoretic lower bounds on the oracle complexity of convex optimization. Advances in Neural Information Processing Systems 22 (2009).

3. Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

4. 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).

5. SIAM review 60, 2;Bottou Léon,2018

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. Multi-modal Data Processing for Foundation Models: Practical Guidances and Use Cases;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24