<ul><li>The paper introduces a framework called FLIP to evaluate federated prompt learning algorithms.</li><li>FLIP assesses the performance of 8 state-of-the-art federated prompt learning methods across various scenarios.</li><li>The findings demonstrate that prompt learning maintains strong generalization performance with minimal resource consumption.</li><li>This work emphasizes the effectiveness of federated prompt learning in data scarcity and cross-domain distributional shift scenarios.</li></ul>

FLIP: Towards Comprehensive and Reliable Evaluation of Federated Prompt Learning

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