Killing Two Birds with One Stone: Quantization Achieves Privacy in Distributed Learning. (arXiv:2304.13545v1 [cs.LG])

Communication efficiency and privacy protection are two critical issues in
distributed machine learning. Existing methods tackle these two issues
separately and may have a high implementation complexity that constrains their
application in a resource-limited environment. We propose a comprehensive
quantization-based solution that could simultaneously achieve communication
efficiency and privacy protection, providing new insights into the correlated
nature of communication and privacy. Specifically, we demonstrate the
effectiveness of our proposed solutions in the distributed stochastic gradient
descent (SGD) framework by adding …

application communication complexity critical distributed efficiency environment framework high insights machine machine learning may nature privacy protection solution solutions