  Unlike traditional distributed machine learning, federated learning stores
data locally for training and then aggregates the models on the server, which
solves the data security problem that may arise in traditional distributed
machine learning. However, during the training process, the transmission of
model parameters can impose a significant load on the network bandwidth. It has
been pointed out that the vast majority of model parameters are redundant
during model parameter transmission. In this paper, we explore the data
distribution law of selected partial model parameters on this basis, and
propose a deep hierarchical quantization compression algorithm, which further
compresses the model and reduces the network load brought by data transmission
through the hierarchical quantization of model parameters. And we adopt a
dynamic sampling strategy for the selection of clients to accelerate the
convergence of the model. Experimental results on different public datasets
demonstrate the effectiveness of our algorithm.
