Client-Customized Adaptation for Parameter-Efficient Federated Learning

Yeachan Kim, Junho Kim, Wing Lam Mok, Jun Hyung Park, Sang Keun Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Despite the versatility of pre-trained language models (PLMs) across domains, their large memory footprints pose significant challenges in federated learning (FL), where the training model has to be distributed between a server and clients. One potential solution to bypass such constraints might be the use of parameter-efficient fine-tuning (PEFT) in the context of FL. However, we have observed that typical PEFT tends to severely suffer from heterogeneity among clients in FL scenarios, resulting in unstable and slow convergence. In this paper, we propose Client-Customized Adaptation (C2A), a novel hypernetwork-based FL framework that generates client-specific adapters by conditioning the client information. With the effectiveness of the hypernetworks in generating customized weights through learning to adopt the different characteristics of inputs, C2A can maximize the utility of shared model parameters while minimizing the divergence caused by client heterogeneity. To verify the efficacy of C2A, we perform extensive evaluations on FL scenarios involving heterogeneity in label and language distributions. Comprehensive evaluation results clearly support the superiority of C2A in terms of both efficiency and effectiveness in FL scenarios.

Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics, ACL 2023
PublisherAssociation for Computational Linguistics (ACL)
Number of pages14
ISBN (Electronic)9781959429623
Publication statusPublished - 2023
Event61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 - Toronto, Canada
Duration: 2023 Jul 92023 Jul 14

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X


Conference61st Annual Meeting of the Association for Computational Linguistics, ACL 2023

Bibliographical note

Publisher Copyright:
© 2023 Association for Computational Linguistics.

ASJC Scopus subject areas

  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics


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