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

• URL: http://arxiv.org/abs/2406.11087v3
• Date: Thu, 15 Aug 2024 22:57:08 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, Xuhong Zhang, Sheng Cheng, Xun Wang, Jianwei Yin, Tianyu Du,
• Abstract summary: We introduce DP-MemArc, a novel training framework aimed at reducing the memory costs of large language models. DP-MemArc incorporates side network or reversible network designs to support a variety of differential privacy memory-efficient fine-tuning schemes.
• Score: 29.147695134795146
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 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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