Private inference (PI) has emerged as a promising solution to execute computations on encrypted data, safeguarding user privacy and model parameters in edge computing. However, existing PI methods are predominantly developed considering constant resource constraints, overlooking the varied and dynamic resource constraints in diverse edge devices, like energy budgets. Consequently, model providers have to design specialized models for different devices, where all of them have to be stored on the edge server, resulting in inefficient deployment. To fill this gap, this work presents AdaPI, a novel approach that achieves adaptive PI by allowing a model to perform well across edge devices with diverse energy budgets. AdaPI employs a PI-aware training strategy that optimizes the model weights alongside weight-level and feature-level soft masks. These soft masks are subsequently transformed into multiple binary masks to enable adjustments in communication and computation workloads. Through sequentially training the model with increasingly dense binary masks, AdaPI attains optimal accuracy for each energy budget, which outperforms the state-of-the-art PI methods by 7.3\% in terms of test accuracy on CIFAR-100. The code of AdaPI can be accessed via https://github.com/jiahuiiiiii/AdaPI.

翻译：隐私推理（PI）已成为一种在加密数据上执行计算的有前景的解决方案，可在边缘计算中保护用户隐私和模型参数。然而，现有的PI方法主要是在考虑恒定资源约束下开发的，忽视了不同边缘设备（如能量预算）中多样且动态的资源约束。因此，模型提供商必须为不同设备设计专门的模型，所有这些模型都必须存储在边缘服务器上，导致部署效率低下。为填补这一空白，本文提出了AdaPI，这是一种新颖的方法，通过允许模型在具有不同能量预算的边缘设备上均表现良好来实现自适应PI。AdaPI采用一种PI感知的训练策略，该策略在优化模型权值的同时，优化权值级和特征级的软掩码。这些软掩码随后被转换为多个二进制掩码，以实现通信和计算工作负载的调整。通过使用逐渐稠密的二进制掩码顺序训练模型，AdaPI为每个能量预算获得了最优的准确率，其在CIFAR-100上的测试准确率比最先进的PI方法高出7.3%。AdaPI的代码可通过 https://github.com/jiahuiiiiii/AdaPI 访问。