FedPETuning: When federated learning meets the parameter-efficient tuning methods of pre-trained language models

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

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


With increasing concerns about data privacy, there is an increasing necessity of fine-tuning pre-trained language models (PLMs) for adapting to downstream tasks located in end-user devices or local clients without transmitting data to the central server. This urgent necessity therefore calls the research of investigating federated learning (FL) for PLMs. However, large PLMs bring the curse of prohibitive communication overhead and local model adaptation costs for the FL system. To this end, we investigate the parameter-efficient tuning (PETuning) of PLMs and develop a corresponding federated benchmark for four representative PETuning methods, dubbed FedPETuning. Specifically, FedPETuning provides the first holistic empirical study of representative PLMs tuning methods in FL, covering privacy attacks, performance comparisons, and resource-constrained analysis. Intensive experimental results have indicated that FedPETuning can efficiently defend against privacy attacks and maintains acceptable performance with reducing heavy resource consumption. The open-source code and data are available at https://github.com/SMILELab-FL/FedPETuning.
Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics: ACL 2023
EditorsAnna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Place of PublicationStroudsburg PA USA
PublisherAssociation for Computational Linguistics (ACL)
Number of pages15
ISBN (Electronic)9781959429623
Publication statusPublished - 2023
EventAnnual Meeting of the Association of Computational Linguistics 2023 - Toronto, Canada
Duration: 9 Jul 202314 Jul 2023
Conference number: 61st
https://aclanthology.org/volumes/2023.acl-long/ (Proceedings - 1)
https://aclanthology.org/volumes/2023.findings-acl/ (Proceedings - 2)
https://2023.aclweb.org/ (Website)


ConferenceAnnual Meeting of the Association of Computational Linguistics 2023
Abbreviated titleACL 2023
Internet address

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