  Federated learning is a promising framework to mitigate data privacy and
computation concerns. However, the communication cost between the server and
clients has become the major bottleneck for successful deployment. Despite
notable progress in gradient compression, the existing quantization methods
require further improvement when low-bits compression is applied, especially
the overall systems often degenerate a lot when quantization are applied in
double directions to compress model weights and gradients. In this work, we
propose a simple cosine-based nonlinear quantization and achieve impressive
results in compressing round-trip communication costs. We are not only able to
compress model weights and gradients at higher ratios than previous methods,
but also achieve competing model performance at the same time. Further, our
approach is highly suitable for federated learning problems since it has low
computational complexity and requires only a little additional data to recover
the compressed information. Extensive experiments have been conducted on image
classification and brain tumor semantic segmentation using the CIFAR-10, and
BraTS datasets where we show state-of-the-art effectiveness and impressive
communication efficiency.
