Abstract
Condition injection enables diffusion models to generate context-aware outputs, which is essential for many time-series tasks. However, heterogeneous conditional contexts (e.g., observed history, missingness patterns or outlier covariates) can induce heavy-tailed per-example gradients. Under Differentially Private Stochastic Gradient Descent (DP-SGD), these rare conditioning-driven heavy-tailed gradients disproportionately trigger global clipping, resulting in outlier-dominated updates, larger clipping bias, and degraded utility under a fixed privacy budget. In this paper, we propose DP-aware AdaLN-Zero, a drop-in sensitivity-aware conditioning mechanism for conditional diffusion transformers that limits conditioning-induced gain without modifying the DP-SGD mechanism. DP-aware AdaLN-Zero jointly constrains conditioning representation magnitude and AdaLN modulation parameters via bounded re-parameterization, suppressing extreme gradient tail events before gradient clipping and noise injection. Empirically, DP-SGD equipped with DP-aware AdaLN-Zero improves interpolation/imputation and forecasting under matched privacy settings. We observe consistent gains on a real-world power dataset and two public ETT benchmarks over vanilla DP-SGD. Moreover, gradient diagnostics attribute these improvements to conditioning-specific tail reshaping and reduced clipping distortion, while preserving expressiveness in non-private training. Overall, these results show that sensitivity-aware conditioning can substantially improve private conditional diffusion training without sacrificing standard performance.
Abstract (translated)
条件注入使得扩散模型能够生成上下文感知的输出，这对于许多时间序列任务至关重要。然而，异构条件上下文（例如观察历史、缺失模式或异常协变量）可能会导致长尾分布的梯度。在差异隐私随机梯度下降法(DP-SGD)中，这些罕见的由条件驱动的重尾梯度会不恰当地触发全局剪切操作，从而导致异常值主导的更新过程，并产生更大的剪切偏差和固定的隐私预算下的功能降级。 本文提出了一种新的方法DP-aware AdaLN-Zero，这是一种敏感性感知条件注入机制，适用于有条件扩散变换器，在无需修改DP-SGD机制的情况下限制了由条件引入的变化。DP-aware AdaLN-Zero通过有界重新参数化同时约束条件表示的幅度和AdaLN调制参数，抑制极端梯度尾部事件，并在梯度剪切和噪声添加之前消除这些异常值。 实验表明，配备了DP-aware AdaLN-Zero的DP-SGD在匹配隐私设置下提高了插值/填补和预测性能。我们在一个真实世界电力数据集以及两个公开ETT基准测试中观察到了相对于未改进的DP-SGD的一致性收益。此外，梯度诊断将这些改进归因于条件特定尾部重塑和剪切失真的减少，并在非隐私训练过程中保持了表达能力。 总体而言，这些结果表明敏感性感知条件注入可以在不牺牲标准性能的情况下显著提高私人有条件扩散训练的效果。
URL
https://arxiv.org/abs/2602.22610
