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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 17·5 min read
P2Skill: Privacy Preserving Skill Distillation for Cloud-Local LLM Inference Systems
TL;DR
P2Skill introduces a novel method for privacy-preserving skill distillation in cloud-local large language model (LLM) inference systems, enhancing user data protection. It achieves 1.69x and 3.66x higher privacy-preserved inference quality compared to existing methods.
✦ Why It Matters
Implement P2Skill to enhance privacy in your AI applications without extensive retraining.
Key Takeaways
- Adopt P2Skill for improved privacy in AI systems.
- Utilize prompt-based skill distillation for better context handling.
- Leverage cloud LLM feedback for iterative skill refinement.
- Avoid extensive retraining with P2Skill's approach.
How It Works
P2Skill utilizes a prompt-based skill distillation method where a small local language model (SLM) autonomously performs tasks like decomposition and paraphrasing. It routes information while being aware of personally identifiable information (PII) and refines its skills based on feedback from a cloud LLM.
This iterative process allows the SLM to generalize beyond memorized PII patterns, enhancing its ability to maintain privacy without requiring additional training.