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Federated Learning (FL) is often impeded by communication overhead issues.Prompt tuning, as a potential solution, has been introduced to only adjust afew trainable parameters rather than the whole model. However, currentsingle-modality prompt tuning approaches fail to comprehensively portray localclients' data. To overcome this limitation, we present Twin Prompt Federatedlearning (TPFL), a pioneering solution that integrates both visual and textualmodalities, ensuring a more holistic representation of local clients' datacharacteristics. Furthermore, in order to tackle the data heterogeneity issues,we introduce the Augmented TPFL (ATPFL) employing the contrastive learning toTPFL, which not only enhances the global knowledge acquisition of client modelsbut also fosters the development of robust, compact models. The effectivenessof TPFL and ATPFL is substantiated by our extensive evaluations, consistentlyshowing superior performance compared to all baselines.

Inclusive Data Representation in Federated Learning: A Novel Approach Integrating Textual and Visual Prompt