mlsanityData to 14 Oct 2026

Personalized Federated Learning via Dual-Prompt Optimization and Cross Fusion

arXiv 2506.21144 ↗First posted 26 Jun 2025cs.LG cs.CVcited by 3

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Abstract (arXiv)

Federated learning (FL) enables collaborative model training across decentralized clients without sharing local data, but is challenged by heterogeneity in data, computation, and communication. Pretrained vision-language models (VLMs), with their strong generalization and lightweight tuning via prompts, offer a promising solution. However, existing federated prompt-learning methods rely only on text prompts and overlook joint label-domain distribution shifts. In this paper, we propose a personalized FL framework based on dual-prompt learning and cross fusion, termed pFedDC. Specifically, each client maintains both global and local prompts across vision and language modalities: global prompts capture common knowledge shared across the federation, while local prompts encode client-specific semantics and domain characteristics. Meanwhile, a cross-fusion module is designed to adaptively integrate prompts from different levels, enabling the model to generate personalized representations aligned with each client's unique data distribution. Extensive experiments across nine datasets with various types of heterogeneity show that pFedDC consistently outperforms state-of-the-art methods.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 45 of its 58 references that are in the map. First posted on arXiv 26 Jun 2025.

Builds on

45 of 58 references.

References · 45

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
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33 more
  1. 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,496
  2. 2018Moment Matching for Multi-Source Domain Adaptationcited by 1,331
  3. 2021Model-Contrastive Federated Learningcited by 766
  4. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  5. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  6. 2019ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networkscited by 575
  7. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 571
  8. 2019Stand-Alone Self-Attention in Vision Modelscited by 542
  9. 2019Federated Learning with Personalization Layerscited by 541
  10. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  11. 2017Residual Attention Network for Image Classificationcited by 386
  12. 2020Exploring Self-attention for Image Recognitioncited by 313
  13. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
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  15. 2022PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Modelcited by 134
  16. 2023Personalized Federated Learning with Feature Alignment and Classifier Collaborationcited by 97
  17. 2023FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learningcited by 70
  18. 2023FedDisco: Federated Learning with Discrepancy-Aware Collaborationcited by 67
  19. 2023Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generationcited by 54
  20. 2022Dual Modality Prompt Tuning for Vision-Language Pre-Trained Modelcited by 52
  21. 2023Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Modelscited by 50
  22. 2022Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learningcited by 46
  23. 2024Global and Local Prompts Cooperation via Optimal Transport for Federated Learningcited by 46
  24. 2023Learning Federated Visual Prompt in Null Space for MRI Reconstructioncited by 40
  25. 2022FedTP: Federated Learning by Transformer Personalizationcited by 36
  26. 2023FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policycited by 36
  27. 2024DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learningcited by 32
  28. 2022Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learningcited by 29
  29. 2024Harmonizing Generalization and Personalization in Federated Prompt Learningcited by 27
  30. 2023Text-driven Prompt Generation for Vision-Language Models in Federated Learningcited by 17
  31. 2024Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Modelscited by 17
  32. 2023Dynamic Heterogeneous Federated Learning with Multi-Level Prototypescited by 7
  33. 2024Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalizationcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Cooperative Pseudo Labeling for Unsupervised Federated Classificationcited by 0
  2. 2026Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learningcited by 0
  3. 2026PFAdapter: Hierarchical LoRA Decomposition for Personalized Federated MLLMscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026SDFed: Bridging Local Global Discrepancy via Subspace Refinement and Divergence Control in Federated Prompt Learning
  2. 2025FedMGP: Personalized Federated Learning with Multi-Group Text-Visual Prompts
  3. 2025FedDEAP: Adaptive Dual-Prompt Tuning for Multi-Domain Federated Learning
  4. 2025pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Models
  5. 2024Federated Domain Generalization via Prompt Learning and Aggregation
  6. 2025An Empirical Study of Federated Prompt Learning for Vision Language Model
  7. 2025FLIP: Towards Comprehensive and Reliable Evaluation of Federated Prompt Learning
  8. 2024Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method