  Federated learning is an emerging technique used to prevent the leakage of
private information. Unlike centralized learning that needs to collect data
from users and store them collectively on a cloud server, federated learning
makes it possible to learn a global model while the data are distributed on the
users' devices. However, compared with the traditional centralized approach,
the federated setting consumes considerable communication resources of the
clients, which is indispensable for updating global models and prevents this
technique from being widely used. In this paper, we aim to optimize the
structure of the neural network models in federated learning using a
multi-objective evolutionary algorithm to simultaneously minimize the
communication costs and the global model test errors. A scalable method for
encoding network connectivity is adapted to federated learning to enhance the
efficiency in evolving deep neural networks. Experimental results on both
multilayer perceptrons and convolutional neural networks indicate that the
proposed optimization method is able to find optimized neural network models
that can not only significantly reduce communication costs but also improve the
learning performance of federated learning compared with the standard fully
connected neural networks.
