DP-MemArc introduces a breakthrough framework that significantly reduces memory requirements for large language models while protecting user privacy through differential privacy.

• Memory optimization techniques that make LLM deployment more feasible
• Privacy preservation through differential privacy mechanisms to protect sensitive user data
• Transfer learning approach that maintains model performance while reducing resource demands
• Practical implementation addressing both security and engineering constraints

This research addresses critical security concerns by enabling organizations to deploy powerful language models without compromising user data privacy, making advanced AI more accessible while maintaining confidentiality standards.

DP-MemArc: Differential Privacy Transfer Learning for Memory Efficient Language Models