As an emerging field, federated learning has recently attracted considerable attention.
 Compared to distributed learning in the datacenter setting, federated learning
 has more strict constraints on computate efficiency of the learned model and communication
 cost during the training process. In this work, we propose an efficient
 federated learning framework based on variational dropout. Our approach is able
 to jointly learn a sparse model while reducing the amount of gradients exchanged
 during the iterative training process. We demonstrate the superior performance
 of our approach on achieving significant model compression and communication
 reduction ratios with no accuracy loss.
a joint model and gradient sparsification method for federated learning Applies variational dropout to reduce the communication cost of distributed training of neural networks, and does experiments on mnist, cifar10 and svhn datasets.  The authors propose an algorithm that reduces communication costs in federated learning by sending sparse gradients from device to server and back. Combines distributed optimization algorithm with variational dropout to sparsify the gradients sent to master server from local learners.