Communication Efficient Private Federated Learning Using Dithering
Burak Hasircioglu Deniz Gunduz
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.