  In this paper, we propose the discrete Gaussian based differentially private
federated learning (D2P-Fed), a unified scheme to achieve both differential
privacy (DP) and communication efficiency in federated learning (FL). In
particular, compared with the only prior work taking care of both aspects,
D2P-Fed provides stronger privacy guarantee, better composability and smaller
communication cost. The key idea is to apply the discrete Gaussian noise to the
private data transmission. We provide complete analysis of the privacy
guarantee, communication cost and convergence rate of D2P-Fed. We evaluated
D2P-Fed on INFIMNIST and CIFAR10. The results show that D2P-Fed outperforms
the-state-of-the-art by 4.7% to 13.0% in terms of model accuracy while saving
one third of the communication cost.
