  Large language models have consistently demonstrated remarkable performance
across a wide spectrum of applications. Nonetheless, the deployment of these
models can inadvertently expose user privacy to potential risks. The
substantial memory demands of these models during training represent a
significant resource consumption challenge. The sheer size of these models
imposes a considerable burden on memory resources, which is a matter of
significant concern in practice. In this paper, we present an innovative
training framework MemDPT that not only reduces the memory cost of large
language models but also places a strong emphasis on safeguarding user data
privacy. MemDPT provides edge network and reverse network designs to
accommodate various differential privacy memory-efficient fine-tuning schemes.
Our approach not only achieves $2 \sim 3 \times$ memory optimization but also
provides robust privacy protection, ensuring that user data remains secure and
confidential. Extensive experiments have demonstrated that MemDPT can
effectively provide differential privacy efficient fine-tuning across various
task scenarios.
