With rise of machine learning (ML) and the proliferation of smart mobile
devices, recent years have witnessed a surge of interest in performing ML in
wireless edge networks. In this paper, we consider the problem of jointly
improving data privacy and communication efficiency of distributed edge
learning, both of which are critical performance metrics in wireless edge
network computing. Toward this end, we propose a new decentralized stochastic
gradient method with sparse differential Gaussian-masked stochastic gradients
(SDM-DSGD) for non-convex distributed edge learning. Our main contributions are
three-fold: i) We theoretically establish the privacy and communication
efficiency performance guarantee of our SDM-DSGD method, which outperforms all
existing works; ii) We show that SDM-DSGD improves the fundamental
training-privacy trade-off by {\em two orders of magnitude} compared with the
state-of-the-art. iii) We reveal theoretical insights and offer practical
design guidelines for the interactions between privacy preservation and
communication efficiency, two conflicting performance goals. We conduct
extensive experiments with a variety of learning models on MNIST and CIFAR-10
datasets to verify our theoretical findings. Collectively, our results
contribute to the theory and algorithm design for distributed edge learning.

本文提出了一种新的分散式隨機梯度方法，用於非凸分散式邊緣學習，以提高數據隱私和通信效率的性能指標。該方法在理論上確立了隱私性和通信效率的性能保證，實驗結果證明了該方法能夠兼顧兩者，是現有作品的優秀方法。