@inproceedings{guo-etal-2025-federated,
    title = "Can Federated Learning Safeguard Private Data in {LLM} Training? Vulnerabilities, Attacks, and Defense Evaluation",
    author = "Guo, Wenkai  and
      Liu, Xuefeng  and
      Wang, Haolin  and
      Niu, Jianwei  and
      Tang, Shaojie  and
      Yuan, Jing",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1303/",
    doi = "10.18653/v1/2025.findings-emnlp.1303",
    pages = "23986--24013",
    ISBN = "979-8-89176-335-7"
}