@article{azam-etal-2026-lightweight,
    title = "Lightweight Cross-Lingual Federated Prompt Tuning for Low-Resource Languages",
    author = "Azam, Ubaid  and
      Razzak, Imran  and
      Jameel, Shoaib",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.260/",
    pages = "3304--3316",
    abstract = "Multilingual NLP faces challenges of data heterogeneity, privacy, and limited computational resources, especially for low-resource languages. Centralised methods risk privacy breaches, while federated learning struggles with communication overhead and poor cross-lingual generalisation. We propose FLiP (Federated Lightweight Prompt-tuning), a privacy-preserving, resource-efficient, generalizable framework integrating prompt-based learning with federated optimisation. FLiP eliminates communication overhead, reduces trainable parameters to 16{\%}, and cuts GPU memory use by 90{\%}. Experiments show superior generalisation and efficiency under both IID and Non-IID settings, establishing FLiP as a scalable, privacy-aware solution for multilingual NLP, particularly in low-resource and indigenous language contexts."
}