Related papers: DPZero: Private Fine-Tuning of Language Models without Backpropagation

DPZero: Private Fine-Tuning of Language Models without Backpropagation

URL: http://arxiv.org/abs/2310.09639v3
Date: Thu, 6 Jun 2024 14:31:03 GMT
Title: DPZero: Private Fine-Tuning of Language Models without Backpropagation
Authors: Liang Zhang, Bingcong Li, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He,
Abstract summary: We introduce DPZero, a novel private zeroth-order algorithm with nearly dimension-independent rates. The memory efficiency of DPZero is demonstrated in privately fine-tuning RoBERTa and OPT on several downstream tasks.
Score: 49.365749361283704
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: The widespread practice of fine-tuning large language models (LLMs) on domain-specific data faces two major challenges in memory and privacy. First, as the size of LLMs continues to grow, the memory demands of gradient-based training methods via backpropagation become prohibitively high. Second, given the tendency of LLMs to memorize training data, it is important to protect potentially sensitive information in the fine-tuning data from being regurgitated. Zeroth-order methods, which rely solely on forward passes, substantially reduce memory consumption during training. However, directly combining them with standard differentially private gradient descent suffers more as model size grows. To bridge this gap, we introduce DPZero, a novel private zeroth-order algorithm with nearly dimension-independent rates. The memory efficiency of DPZero is demonstrated in privately fine-tuning RoBERTa and OPT on several downstream tasks. Our code is available at https://github.com/Liang137/DPZero.

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