FLIP: Towards Comprehensive and Reliable Evaluation of Federated Prompt Learning

• URL: http://arxiv.org/abs/2503.22263v1
• Date: Fri, 28 Mar 2025 09:27:20 GMT
• Title: FLIP: Towards Comprehensive and Reliable Evaluation of Federated Prompt Learning
• Authors: Dongping Liao, Xitong Gao, Yabo Xu, Chengzhong Xu,
• Abstract summary: We introduce a comprehensive framework, named FLIP, to evaluate federated prompt learning algorithms.<n>FLIP assesses the performance of 8 state-of-the-art federated prompt learning methods across 4 federated learning protocols and 12 open datasets.<n>Our findings demonstrate that prompt learning maintains strong generalization performance in both in-distribution and out-of-distribution settings with minimal resource consumption.
• Score: 18.79033094563453
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The increasing emphasis on privacy and data security has driven the adoption of federated learning, a decentralized approach to train machine learning models without sharing raw data. Prompt learning, which fine-tunes prompt embeddings of pretrained models, offers significant advantages in federated settings by reducing computational costs and communication overheads while leveraging the strong performance and generalization capabilities of vision-language models such as CLIP. This paper addresses the intersection of federated learning and prompt learning, particularly for vision-language models. In this work, we introduce a comprehensive framework, named FLIP, to evaluate federated prompt learning algorithms. FLIP assesses the performance of 8 state-of-the-art federated prompt learning methods across 4 federated learning protocols and 12 open datasets, considering 6 distinct evaluation scenarios. Our findings demonstrate that prompt learning maintains strong generalization performance in both in-distribution and out-of-distribution settings with minimal resource consumption. This work highlights the effectiveness of federated prompt learning in environments characterized by data scarcity, unseen classes, and cross-domain distributional shifts. We open-source the code for all implemented algorithms in FLIP to facilitate further research in this domain.

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