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.

在受信任的聚合器模型中，通过在客户端使用基于减法抖动的量化方案，我们提出了一种既能保护隐私又能确保高效通信的解决方案，实现了两个目标的同时。我们证明了这种方案在复制聚合器中常规噪声添加过程方面的有效性。这意味着与传输完整精度梯度并使用中心噪声添加相比，我们可以确保相同级别的差分隐私，并大幅减少所需的通信量。我们还通过实验证明了我们提出的方法与完整精度梯度方法的准确性相当。