arXiv Machine Learning September 3, 2026 By Quan Minh Nguyen, Hoang M. Ngo, Trong Nghia Hoang, My T. Thai

D-FROST: Decentralized Federated pRompt-tuning via Optimal tranSporT for Non-IID and Imbalanced Data

Read the original on arXiv Machine Learning →

The paper introduces D-FROST, an optimal‑transport‑based algorithm for decentralized federated prompt‑tuning, addressing challenges of heterogeneous local data and non‑IID distributions. It formulates prompt tuning as a Wasserstein optimization over prompt measures and merges neighborhood prompts into compact representative sets. The authors provide theoretical guarantees on consensus error and convergence, and demonstrate effectiveness through experiments on heterogeneous client data.

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