  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 binomial noise to the uniformly quantized
gradients to reach the desired differential privacy level but with a minor
sacrifice in communication efficiency. We theoretically capture the new
trade-offs between communication, privacy, and learning performance.
