An Empirical Study of Federated Prompt Learning for Vision Language Model

This paper explores the application of prompt learning techniquesboth language and vision promptsin federated learning environments, focusing on challenges like data heterogeneity, label skew, and domain shift. Through extensive experiments, it provides insights into optimizing federated prompt learning strategies to improve robustness and deployment of vision-language models in privacy-preserving settings.