research-article

pFedPrompt: Learning Personalized Prompt for Vision-Language Models in Federated Learning

Authors:

Junxiao WangAuthors Info & Claims

WWW '23: Proceedings of the ACM Web Conference 2023

Pages 1364 - 1374

https://doi.org/10.1145/3543507.3583518

Published: 30 April 2023 Publication History

Abstract

Pre-trained vision-language models like CLIP show great potential in learning representations that capture latent characteristics of users. A recently proposed method called Contextual Optimization (CoOp) introduces the concept of training prompt for adapting pre-trained vision-language models. Given the lightweight nature of this method, researchers have migrated the paradigm from centralized to decentralized system to innovate the collaborative training framework of Federated Learning (FL). However, current prompt training in FL mainly focuses on modeling user consensus and lacks the adaptation to user characteristics, leaving the personalization of prompt largely under-explored. Researches over the past few years have applied personalized FL (pFL) approaches to customizing models for heterogeneous users. Unfortunately, we find that with the variation of modality and training behavior, directly applying the pFL methods to prompt training leads to insufficient personalization and performance. To bridge the gap, we present pFedPrompt, which leverages the unique advantage of multimodality in vision-language models by learning user consensus from linguistic space and adapting to user characteristics in visual space in a non-parametric manner. Through this dual collaboration, the learned prompt will be fully personalized and aligned to the user’s local characteristics. We conduct extensive experiments across various datasets under the FL setting with statistical heterogeneity. The results demonstrate the superiority of our pFedPrompt against the alternative approaches with robust performance.

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Cited By

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Feng YMa FLin WYao CChen JYang YGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)FedPAM: Federated Personalized Augmentation Model for Text-to-Image RetrievalProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3657627(1185-1189)Online publication date: 30-May-2024
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Yu HYang XGao XKang YWang HZhang JLi TBaeza-Yates RBonchi F(2024)Personalized Federated Continual Learning via Multi-Granularity PromptProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671948(4023-4034)Online publication date: 25-Aug-2024
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Index Terms

pFedPrompt: Learning Personalized Prompt for Vision-Language Models in Federated Learning
1. Human-centered computing
  1. Collaborative and social computing
    1. Empirical studies in collaborative and social computing
  2. Ubiquitous and mobile computing
    1. Empirical studies in ubiquitous and mobile computing
2. Social and professional topics
  1. User characteristics

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WWW '23: Proceedings of the ACM Web Conference 2023

April 2023

4293 pages

ISBN:9781450394161

DOI:10.1145/3543507

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Published: 30 April 2023

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Key-Area Research and Development Program of Guangdong Province
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WWW '23: The ACM Web Conference 2023

April 30 - May 4, 2023

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