DPQuant: Efficient and Differentially-Private Model Training via Dynamic Quantization Scheduling

• URL: http://arxiv.org/abs/2509.03472v1
• Date: Wed, 03 Sep 2025 16:51:26 GMT
• Title: DPQuant: Efficient and Differentially-Private Model Training via Dynamic Quantization Scheduling
• Authors: Yubo Gao, Renbo Tu, Gennady Pekhimenko, Nandita Vijaykumar,
• Abstract summary: Differentially-Private SGD (DP-SGD) is a powerful technique to protect user privacy when using sensitive data to train neural networks.<n>We show that quantization causes significantly higher accuracy degradation in DP-SGD compared to regular SGD.<n>We present QPQuant, a dynamic quantization framework that adaptively selects a changing subset of layers to quantize at each epoch.
• Score: 7.79764032127686
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Differentially-Private SGD (DP-SGD) is a powerful technique to protect user privacy when using sensitive data to train neural networks. During training, converting model weights and activations into low-precision formats, i.e., quantization, can drastically reduce training times, energy consumption, and cost, and is thus a widely used technique. In this work, we demonstrate that quantization causes significantly higher accuracy degradation in DP-SGD compared to regular SGD. We observe that this is caused by noise injection in DP-SGD, which amplifies quantization variance, leading to disproportionately large accuracy degradation. To address this challenge, we present QPQuant, a dynamic quantization framework that adaptively selects a changing subset of layers to quantize at each epoch. Our method combines two key ideas that effectively reduce quantization variance: (i) probabilistic sampling of the layers that rotates which layers are quantized every epoch, and (ii) loss-aware layer prioritization, which uses a differentially private loss sensitivity estimator to identify layers that can be quantized with minimal impact on model quality. This estimator consumes a negligible fraction of the overall privacy budget, preserving DP guarantees. Empirical evaluations on ResNet18, ResNet50, and DenseNet121 across a range of datasets demonstrate that DPQuant consistently outperforms static quantization baselines, achieving near Pareto-optimal accuracy-compute trade-offs and up to 2.21x theoretical throughput improvements on low-precision hardware, with less than 2% drop in validation accuracy.

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