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arXiv · 2508.12399

Federated Cross-Modal Style-Aware Prompt Generation

Suraj Prasad·Navyansh Mahla·Sunny Gupta·Amit Sethi
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Abstract

Prompt learning has propelled vision-language models like CLIP to excel in diverse tasks, making them ideal for federated learning due to computational efficiency. However, conventional approaches that rely solely on final-layer features miss out on rich multi-scale visual cues and domain-specific style variations in decentralized client data. To bridge this gap, we introduce FedCSAP (Federated Cross-Modal Style-Aware Prompt Generation). Our framework harnesses low, mid, and high-level features from CLIP's vision encoder alongside client-specific style indicators derived from batch-level statistics. By merging intricate visual details with textual context, FedCSAP produces robust, context-aware prompt tokens that are both distinct and non-redundant, thereby boosting generalization across seen and unseen classes. Operating within a federated learning paradigm, our approach ensures data privacy through local training and global aggregation, adeptly handling non-IID class distributions and diverse domain-specific styles. Comprehensive experiments on multiple image classification datasets confirm that FedCSAP outperforms existing federated prompt learning methods in both accuracy and overall generalization.

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Suraj Prasad, Navyansh Mahla, Sunny Gupta, Amit Sethi. 2025-08-17. Federated Cross-Modal Style-Aware Prompt Generation. https://arxiv.org/abs/2508.12399

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