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

• URL: http://arxiv.org/abs/2406.11087v5
• Date: Thu, 20 Feb 2025 07:47:17 GMT
• Title: DP-MemArc: Differential Privacy Transfer Learning for Memory Efficient Language Models
• Authors: Yanming Liu, Xinyue Peng, Yuwei Zhang, Xiaolan Ke, Songhang Deng, Jiannan Cao, Chen Ma, Mengchen Fu, Tianyu Du, Sheng Cheng, Xun Wang, Jianwei Yin, Xuhong Zhang,
• Abstract summary: We introduce DP-MemArc, a novel training framework aimed at reducing the memory costs of large language models. We show that DP-MemArc effectively provides differential privacy-efficient fine-tuning across different task scenarios.
• Score: 29.147695134795146
• License:
• Abstract: Large language models have repeatedly shown outstanding performance across diverse applications. However, deploying these models can inadvertently risk user privacy. The significant memory demands during training pose a major challenge in terms of resource consumption. This substantial size places a heavy load on memory resources, raising considerable practical concerns. In this paper, we introduce DP-MemArc, a novel training framework aimed at reducing the memory costs of large language models while emphasizing the protection of user data privacy. DP-MemArc incorporates side network or reversible network designs to support a variety of differential privacy memory-efficient fine-tuning schemes. Our approach not only achieves about 2.5 times in memory optimization but also ensures robust privacy protection, keeping user data secure and confidential. Extensive experiments have demonstrated that DP-MemArc effectively provides differential privacy-efficient fine-tuning across different task scenarios.

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