  Federated learning (FL) enables a neural network (NN) to be trained using
privacy-sensitive data on mobile devices while retaining all the data on their
local storages. However, FL asks the mobile devices to perform heavy
communication and computation tasks, i.e., devices are requested to upload and
download large-volume NN models and train them. This paper proposes a novel
unsupervised pre-training method adapted for FL, which aims to reduce both the
communication and computation costs through model compression. Since the
communication and computation costs are highly dependent on the volume of NN
models, reducing the volume without decreasing model performance can reduce
these costs. The proposed pre-training method leverages unlabeled data, which
is expected to be obtained from the Internet or data repository much more
easily than labeled data. The key idea of the proposed method is to obtain a
``good'' subnetwork from the original NN using the unlabeled data based on the
lottery hypothesis. The proposed method trains an original model using a
denoising auto encoder with the unlabeled data and then prunes small-magnitude
parameters of the original model to generate a small but good subnetwork. The
proposed method is evaluated using an image classification task. The results
show that the proposed method requires 35\% less traffic and computation time
than previous methods when achieving a certain test accuracy.
