mlsanityData to 7 Oct 2026

FedDEAP: Adaptive Dual-Prompt Tuning for Multi-Domain Federated Learning

arXiv 2510.18837 ↗First posted 21 Oct 2025cs.CVcited by 1

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Abstract (arXiv)

Federated learning (FL) enables multiple clients to collaboratively train machine learning models without exposing local data, balancing performance and privacy. However, domain shift and label heterogeneity across clients often hinder the generalization of the aggregated global model. Recently, large-scale vision-language models like CLIP have shown strong zero-shot classification capabilities, raising the question of how to effectively fine-tune CLIP across domains in a federated setting. In this work, we propose an adaptive federated prompt tuning framework, FedDEAP, to enhance CLIP's generalization in multi-domain scenarios. Our method includes the following three key components: (1) To mitigate the loss of domain-specific information caused by label-supervised tuning, we disentangle semantic and domain-specific features in images by using semantic and domain transformation networks with unbiased mappings; (2) To preserve domain-specific knowledge during global prompt aggregation, we introduce a dual-prompt design with a global semantic prompt and a local domain prompt to balance shared and personalized information; (3) To maximize the inclusion of semantic and domain information from images in the generated text features, we align textual and visual representations under the two learned transformations to preserve semantic and domain consistency. Theoretical analysis and extensive experiments on four datasets demonstrate the effectiveness of our method in enhancing the generalization of CLIP for federated image recognition across multiple domains.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 36 of its 52 references that are in the map. First posted on arXiv 21 Oct 2025.

Builds on

36 of 52 references.

References · 36

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24 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approachcited by 0

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  4. 2025FedMVP: Federated Multimodal Visual Prompt Tuning for Vision-Language Models
  5. 2024Harmonizing Generalization and Personalization in Federated Prompt Learning
  6. 2023Text-driven Prompt Generation for Vision-Language Models in Federated Learning
  7. 2025Federated Cross-Modal Style-Aware Prompt Generation
  8. 2024Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models