FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models-Reference-Cited by-同舟云学术

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FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models

Published:2023 Issue: Volume: Page:
ISSN:
Container-title:Findings of the Association for Computational Linguistics: ACL 2023
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Author:

Zhang Zhuo,Yang Yuanhang,Dai Yong,Wang Qifan,Yu Yue,Qu Lizhen,Xu Zenglin

Publisher

Association for Computational Linguistics

Cited by 6 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. FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. Federated Learning for Vehicle Trajectory Prediction: Methodology and Benchmark Study;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

3. Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly;Proceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning;2024-06-09

4. Applications and Challenges for Large Language Models: From Data Management Perspective;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

5. Assortment of Attention Heads: Accelerating Federated Peft with Head Pruning and Strategic Client Selection;2024

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