Next-generation wireless networks, such as edge intelligence and wireless distributed learning, face two critical challenges: communication efficiency and privacy protection. In this work, our focus is on addressing these issues in a distributed learning framework. We consider a new approach that simultaneously achieves communication efficiency and privacy protection by exploiting the privacy advantage offered by quantization. Specifically, we use a quantization scheme called \textbf{Gau}ssian \textbf{L}ayered \textbf{R}andomized \textbf{Q}uantization (Gau-LRQ) that compresses the raw model gradients using a layer multishift coupler. By adjusting the parameters of Gau-LRQ, we shape the quantization error to follow the expected Gaussian distribution, thus ensuring client-level differential privacy (CLDP). We demonstrate the effectiveness of our proposed Gau-LRQ in the distributed stochastic gradient descent (SGD) framework and theoretically quantify the trade-offs between communication, privacy, and convergence performance. We further improve the convergence performance by enabling dynamic private budget and quantization bit allocation. We achieve this by using an optimization formula that minimizes convergence error subject to the privacy budget constraint. We evaluate our approach on multiple datasets, including MNIST, CIFAR-10, and CIFAR-100, and show that our proposed method outperforms the baselines in terms of learning performance under various privacy constraints. Moreover, we observe that dynamic privacy allocation yields additional accuracy improvements for the models compared to the fixed scheme.

翻译：下一代无线网络（如边缘智能和无线分布式学习）面临两个关键挑战：通信效率与隐私保护。本研究聚焦于在分布式学习框架中解决这些问题。我们提出一种新方法，通过利用量化带来的隐私优势，同时实现通信效率与隐私保护。具体而言，我们采用名为**高斯分层随机量化**（Gau-LRQ）的量化方案，利用层级多移位耦合器压缩原始模型梯度。通过调整Gau-LRQ参数，我们使量化误差服从预期的高斯分布，从而确保客户端级差分隐私（CLDP）。我们在分布式随机梯度下降（SGD）框架中验证了所提Gau-LRQ的有效性，并从理论上量化了通信、隐私与收敛性能之间的权衡关系。进一步地，我们通过动态隐私预算与量化比特分配优化收敛性能，具体采用最小化隐私预算约束下收敛误差的优化公式实现。在MNIST、CIFAR-10和CIFAR-100等多个数据集上的评估表明，所提方法在多种隐私约束下的学习性能均优于基准方法。此外，我们发现相较于固定分配方案，动态隐私分配能为模型带来额外的精度提升。