mlsanityData to 14 Oct 2026

Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data

arXiv 2502.19752 ↗First posted 27 Feb 2025cs.LG cs.AIcited by 9

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Abstract (arXiv)

Fine-tuning pre-trained models is a popular approach in machine learning for solving complex tasks with moderate data. However, fine-tuning the entire pre-trained model is ineffective in federated data scenarios where local data distributions are diversely skewed. To address this, we explore integrating federated learning with a more effective prompt-tuning method, optimizing for a small set of input prefixes to reprogram the pre-trained model's behavior. Our approach transforms federated learning into a distributed set modeling task, aggregating diverse sets of prompts to globally fine-tune the pre-trained model. We benchmark various baselines based on direct adaptations of existing federated model aggregation techniques and introduce a new probabilistic prompt aggregation method that substantially outperforms these baselines. Our reported results on a variety of computer vision datasets confirm that the proposed method is most effective to combat extreme data heterogeneity in federated learning.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 39 of its 60 references that are in the map. First posted on arXiv 27 Feb 2025.

Builds on

39 of 60 references.

References · 39

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27 more
  1. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  2. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  3. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  4. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  5. 2019Federated Learning with Personalization Layerscited by 541
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  15. 2022FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learningcited by 91
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  19. 2023Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generationcited by 54
  20. 2021DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptationcited by 37
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 4.

9 papers.

  1. 2025Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Datacited by 8
  2. 2026Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Datacited by 3
  3. 2025FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Modelscited by 2
  4. 2025SABRE-FL: Selective and Accurate Backdoor Rejection for Federated Prompt Learningcited by 2
  5. 2025C^2Prompt: Class-aware Client Knowledge Interaction for Federated Continual Learningcited by 2
  6. 2026Leveraging Soft Prompts for Privacy Attacks in Federated Prompt Tuningcited by 1
  7. 2026Rethinking Cross-Modal Fine-Tuning: Optimizing the Interaction Between Feature Alignment and Target Fittingcited by 1
  8. 2025OvA-LP: A Simple and Efficient Framework for Federated Learning on Non-IID Datacited by 0
  9. 2026Nonparametric Distribution Matching for Self-Supervised Whole-Slide Image Condensationcited by 0