RQP-SGD: Differential Private Machine Learning through Noisy SGD and Randomized Quantization

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 …

context cs.ai cs.cr cs.lg data data processing demand devices differential edge iot large models machine machine learning parameters processing prove quantization real-time through train