  The conventional machine learning (ML) and deep learning approaches need to
share customers' sensitive information with an external credit bureau to
generate a prediction model that opens the door to privacy leakage. This
leakage risk makes financial companies face an enormous challenge in their
cooperation. Federated learning is a machine learning setting that can protect
data privacy, but the high communication cost is often the bottleneck of the
federated systems, especially for large neural networks. Limiting the number
and size of communications is necessary for the practical training of large
neural structures. Gradient sparsification has received increasing attention as
a method to reduce communication cost, which only updates significant gradients
and accumulates insignificant gradients locally. However, the secure
aggregation framework cannot directly use gradient sparsification. This article
proposes two sparsification methods to reduce communication cost in federated
learning. One is a time-varying hierarchical sparsification method for model
parameter update, which solves the problem of maintaining model accuracy after
high ratio sparsity. It can significantly reduce the cost of a single
communication. The other is to apply the sparsification method to the secure
aggregation framework. We sparse the encryption mask matrix to reduce the cost
of communication while protecting privacy. Experiments show that under
different Non-IID experiment settings, our method can reduce the upload
communication cost to about 2.9% to 18.9% of the conventional federated
learning algorithm when the sparse rate is 0.01.
