  Federated Learning (FL) is an emerging decentralized learning framework
through which multiple clients can collaboratively train a learning model.
However, a major obstacle that impedes the wide deployment of FL lies in
massive communication traffic. To train high dimensional machine learning
models (such as CNN models), heavy communication traffic can be incurred by
exchanging model updates via the Internet between clients and the parameter
server (PS), implying that the network resource can be easily exhausted.
Compressing model updates is an effective way to reduce the traffic amount.
However, a flexible unbiased compression algorithm applicable for both uplink
and downlink compression in FL is still absent from existing works. In this
work, we devise the Model Update Compression by Soft Clustering (MUCSC)
algorithm to compress model updates transmitted between clients and the PS. In
MUCSC, it is only necessary to transmit cluster centroids and the cluster ID of
each model update. Moreover, we prove that: 1) The compressed model updates are
unbiased estimation of their original values so that the convergence rate by
transmitting compressed model updates is unchanged; 2) MUCSC can guarantee that
the influence of the compression error on the model accuracy is minimized.
Then, we further propose the boosted MUCSC (B-MUCSC) algorithm, a biased
compression algorithm that can achieve an extremely high compression rate by
grouping insignificant model updates into a super cluster. B-MUCSC is suitable
for scenarios with very scarce network resource. Ultimately, we conduct
extensive experiments with the CIFAR-10 and FEMNIST datasets to demonstrate
that our algorithms can not only substantially reduce the volume of
communication traffic in FL, but also improve the training efficiency in
practical networks.
