arXiv:2606.01386v1 Announce Type: cross Abstract: 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 constrai
The increasing emphasis on data privacy and sovereign data control, combined with rapid advancements in AI, makes privacy-preserving solutions like federated learning critical for public sector AI adoption.
This development showcases a practical application of AI that addresses inherent public sector challenges around data sensitivity and regulatory compliance, potentially accelerating its integration into government services.
The ability to deploy powerful AI models on sensitive, non-centralized data sources changes the scope of what government AI can achieve while upholding privacy standards.
- · Italian Public Administration
- · Federated Learning providers
- · Citizens seeking enhanced privacy
- · AI developers in regulated industries
- · Traditional centralized AI model providers
- · Data brokers relying on unrestricted data pooling
Increased adoption of privacy-preserving AI architectures in national public administrations.
Development of specialized federated learning platforms tailored for specific national regulatory environments.
A global race among nations to develop and implement sovereign, privacy-preserving AI for critical public services, potentially reducing reliance on foreign AI stacks.
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Read at arXiv cs.CL