The rise of IoT devices has prompted the demand for deploying machine
learning at-the-edge with real-time, efficient, and secure data processing. In
this context, implementing machine learning (ML) models with real-valued weight
parameters can prove to be impractical particularly for large models, and there
is a need to train models with quantized discrete weights. At the same time,
these low-dimensional models also need to preserve privacy of the underlying
dataset. In this work, we present RQP-SGD, a new approach for
privacy-preserving quantization to train machine learning models for low-memory
ML-at-the-edge. This approach combines differentially private stochastic
gradient descent (DP-SGD) with randomized quantization, providing a measurable
privacy guarantee in machine learning. In particular, we study the utility
convergence of implementing RQP-SGD on ML tasks with convex objectives and
quantization constraints and demonstrate its efficacy over deterministic
quantization. Through experiments conducted on two datasets, we show the
practical effectiveness of RQP-SGD.

针对物联网设备需求趋升，要求在边缘实时、高效、安全地处理数据的机器学习需求，本研究提出了一种新的隐私保护量化方法 RQP-SGD，结合差分隐私随机梯度下降（DP-SGD）与随机量化，实现低内存机器学习模型的训练，同时保护底层数据集的隐私并展示了其在凸目标和量化约束下的实用性和有效性。