  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.
