RESEARCH · RESEARCH · #1327
PrivMeSA: privacy-aware self-evolving multi-agent system for clinical LLMs
The arXiv paper introduces PrivMeSA, a privacy-aware self-evolving multi-agent system where a local clinical LLM agent consults remote specialist models while using reinforcement learning to trade off task accuracy against disclosure and re-identification risk; a local lesson memory distills completed consultations into reusable guidance so future cases can avoid remote queries. On an emergency-department benchmark built from MIMIC-IV-ED, PrivMeSA improves mean task accuracy by up to 15.8 percentage points while reducing direct personal-detail disclosures from 98.0% to 0.2% and eliminating cases narrowed to ten or fewer registry patients.
KEY POINTS
- The arXiv paper introduces PrivMeSA, a privacy-aware self-evolving multi-agent system where a local clinical LLM agent consults remote specialist models while using reinforcement learning to trade off task accuracy against disclosure and re-identification risk; a local lesson memory distills completed consultations into reusable guidance so future cases can avoid remote queries.
- On an emergency-department benchmark built from MIMIC-IV-ED, PrivMeSA improves mean task accuracy by up to 15.8 percentage points while reducing direct personal-detail disclosures from 98.0% to 0.2% and eliminating cases narrowed to ten or fewer registry patients.
- Addresses a key privacy-risk in local–remote LLM consultations by learning disclosure control and reusing remote expertise locally, showing large accuracy gains and drastic reductions in disclosure and re-identification risk on clinical data.
WHY IT MATTERS
Addresses a key privacy-risk in local–remote LLM consultations by learning disclosure control and reusing remote expertise locally, showing large accuracy gains and drastic reductions in disclosure and re-identification risk on clinical data.