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D-FROST Enables Efficient Decentralized Federated Prompt-Tuning

Quan Minh Nguyen, Hoang M. Ngo, Trong Nghia Hoang, My T. Thai· September 3, 2026 View original

Key takeaways

  • D-FROST enables efficient decentralized federated prompt-tuning for foundation models.
  • It addresses challenges of non-aligned prompts and heterogeneous data using optimal transport.
  • The algorithm is theoretically guaranteed to achieve consensus and convergence.
  • D-FROST is ideal for privacy-sensitive and resource-constrained distributed AI applications.

Who benefits

HealthcareBFSITelecommunicationsEdge ComputingGovernment

Summary

This paper introduces D-FROST, an optimal-transport-based algorithm for decentralized federated prompt-tuning, addressing challenges like non-aligned prompts and heterogeneous data. It merges neighborhood prompts into compact sets, ensuring consensus and convergence while being parameter-efficient for foundation models.

Prompt tuning offers a parameter-efficient method for adapting large foundation models by only updating a small set of learnable prompts, keeping the main model frozen. This approach is particularly well-suited for decentralized federated learning (DFL), where the high cost of exchanging full model updates is a major hurdle. However, applying prompt tuning in DFL introduces new complexities, primarily because prompts learned from diverse local datasets may not be index-wise aligned, making standard averaging techniques ineffective. This research presents the first study of prompt tuning within a DFL context, formulating it as a Wasserstein-based optimization problem. The proposed algorithm, D-FROST (Decentralized Federated pRompt-tuning via Optimal tranSporT), tackles these challenges by using optimal transport to merge neighborhood prompts into coherent, representative sets. D-FROST is theoretically guaranteed to achieve consensus among clients and make progress toward a shared objective. Empirical results on heterogeneous client data confirm its effectiveness for decentralized prompt tuning, demonstrating a robust solution for adapting foundation models in privacy-preserving and resource-constrained distributed environments.

Why it matters

Professionals working with large language models in privacy-sensitive or resource-constrained environments can leverage D-FROST to efficiently adapt models without centralizing data, enabling new applications in healthcare, finance, and edge AI.

How to implement this in your domain

  1. 1Evaluate D-FROST for fine-tuning foundation models in federated learning setups with non-IID data.
  2. 2Develop proof-of-concept applications for privacy-preserving AI in sensitive domains like healthcare.
  3. 3Integrate optimal transport techniques into existing federated learning frameworks.
  4. 4Benchmark D-FROST's performance against other federated prompt-tuning methods in terms of efficiency and accuracy.

Original post by Quan Minh Nguyen, Hoang M. Ngo, Trong Nghia Hoang, My T. Thai

"arXiv:2609.01802v1 Announce Type: new Abstract: Prompt tuning provides a parameter-efficient way to adapt foundation models (FMs) by freezing the pretrained backbone and updating only a small set of learnable prompts. This property makes prompt tuning especially suitable for dece…"

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Originally posted by Quan Minh Nguyen, Hoang M. Ngo, Trong Nghia Hoang, My T. Thai on X · view source

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