The task of preserving privacy while ensuring efficient communication is a fundamental challenge in federated learning. In this work, we tackle this challenge in the trusted aggregator model, and propose a solution that achieves both objectives simultaneously. We show that employing a quantization scheme based on subtractive dithering at the clients can effectively replicate the normal noise addition process at the aggregator. This implies that we can guarantee the same level of differential privacy against other clients while substantially reducing the amount of communication required, as opposed to transmitting full precision gradients and using central noise addition. We also experimentally demonstrate that the accuracy of our proposed approach matches that of the full precision gradient method.

翻译：在联邦学习中，如何在确保通信效率的同时保护隐私是一项根本挑战。本研究在可信聚合器模型下应对这一挑战，提出了一种能同时实现这两个目标的解决方案。我们证明，客户端采用基于减性抖动的量化方案，可以有效复现聚合器端的高斯噪声添加过程。这意味着与其他客户端相比，我们可以在保证相同差分隐私保护水平的同时，显著降低所需的通信量（无需传输全精度梯度并使用中心噪声添加机制）。实验也表明，所提出方法的准确率与全精度梯度方法相当。