  Deep learning has achieved great success in many applications. However, its
deployment in practice has been hurdled by two issues: the privacy of data that
has to be aggregated centrally for model training and high communication
overhead due to transmission of a large amount of data usually geographically
distributed. Addressing both issues is challenging and most existing works
could not provide an efficient solution. In this paper, we develop FedPC, a
Federated Deep Learning Framework for Privacy Preservation and Communication
Efficiency. The framework allows a model to be learned on multiple private
datasets while not revealing any information of training data, even with
intermediate data. The framework also minimizes the amount of data exchanged to
update the model. We formally prove the convergence of the learning model when
training with FedPC and its privacy-preserving property. We perform extensive
experiments to evaluate the performance of FedPC in terms of the approximation
to the upper-bound performance (when training centrally) and communication
overhead. The results show that FedPC maintains the performance approximation
of the models within $8.5\%$ of the centrally-trained models when data is
distributed to 10 computing nodes. FedPC also reduces the communication
overhead by up to $42.20\%$ compared to existing works.
