Private Compression of Large Language Models
Federated learning approach to create secure, task-specific small models
PPC-GPT introduces a privacy-preserving federated framework that compresses large language models into smaller, task-specific models while protecting sensitive domain knowledge.
- Combines pruning techniques with Chain-of-Thought distillation to reduce model size while maintaining performance
- Implements a server-client federated architecture that keeps private data on local clients
- Addresses both privacy concerns and resource limitations in LLM deployment
- Provides a practical solution for organizations needing secure AI with lower computational requirements
This research is particularly valuable for security-conscious sectors like healthcare and finance that need efficient AI systems without compromising sensitive data.