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.

翻译：大型语言模型已在多种应用中反复展现出卓越性能。然而，部署这些模型可能无意中危及用户隐私。训练过程中巨大的内存需求在资源消耗方面构成了主要挑战，其庞大参数量给内存资源带来沉重负担，引发了显著的实践顾虑。本文提出DP-MemArc——一种旨在降低大型语言模型内存成本并着重保护用户数据隐私的新型训练框架。该框架通过侧网络或可逆网络设计，支持多种差分隐私内存高效微调方案。我们的方法不仅实现了内存优化，同时确保了强健的隐私保护，使用户数据保持安全与机密。大量实验表明，DP-MemArc能在不同任务场景下有效实现差分隐私高效微调。