用联邦学习训练政务聊天机器人，数据不出本地也能高效问答。
GuidaPA: Privacy-Preserving Chatbot for Public Administration via Federated Learning
- 在多个政务平台文档上联邦训练大模型，数据始终保留在本地。
- 模型在隐私保护下达到近私有集中微调的水平，ROUGE-1达61.10。
- 适合需要合规处理敏感数据的政府机构部署使用。
我们提出GuidaPA，一个基于联邦学习（FL）的意大利公共部门（PA）隐私保护聊天机器人，其训练数据来自两个国家级PA平台SIGESON和SIDFORS的公开文档，包括约8页SIGESON手册和31页SIDFORS手册/常见问题。尽管本研究使用公开文档作为安全代理，实际部署将扩展至受限制的内部资料（如工单、工作人员手册、数据库提取），这些数据因监管与组织限制无法集中汇总。GuidaPA集成角色访问控制、客户端安全预处理、非独立同分布（non-IID）效应显式监控，以及参数高效的大型语言模型联邦微调。采用QLoRA（4-bit）进行15轮联邦训练，每客户端按80/20划分训练测试集，通过ROUGE、BLEU-4和METEOR评估答案质量。最佳联邦模型达成ROUGE-1/2/L为61.10/55.77/59.44，BLEU-4为45.02，METEOR为63.94，接近私有集中微调效果，同时实现数据本地化。相比通用基线，领域微调使ROUGE-1从41.45提升至62.18，BLEU-4从26.97提升至50.90。结果表明，联邦学习可在不共享集中数据的前提下，为公共服务提供高质量对话AI。
原文摘要 · Abstract (English)
We present GuidaPA, a privacy-preserving chatbot for the Italian Public Administration (PA) trained via Federated Learning (FL) on documentation from two national PA platforms, SIGESON and SIDFORS. Our corpus includes approximately 8 pages of SIGESON manuals and 31 pages of SIDFORS manuals/FAQs; while this study uses public documentation as a safe proxy, the intended deployment extends to restricted internal sources (e.g., tickets, officer manuals, database extracts) that can not be centrally pooled due to regulatory and organizational constraints. GuidaPA integrates role-based access control, secure client-side preprocessing, explicit monitoring of non-IID effects, and parameter-efficient federated fine-tuning of large language models. Using QLoRA (4-bit) over 15 federated rounds with an 80/20 train-test split per client, we evaluate answer quality with ROUGE, BLEU-4, and METEOR. The best federated model achieves ROUGE-1/2/L of 61.10/55.77/59.44, BLEU-4 of 45.02, and METEOR of 63.94-close to private centralized fine-tuning while keeping data on-site. Compared to the general-purpose baseline, domain fine-tuning improves ROUGE-1 from 41.45 to 62.18 and BLEU-4 from 26.97 to 50.90. Overall, the results indicate that FL can deliver high-quality conversational AI for public services without centralized data sharing
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