  Federated learning (FL) enables the training of a model leveraging
decentralized data in client sites while preserving privacy by not collecting
data. However, one of the significant challenges of FL is limited computation
and low communication bandwidth in resource limited edge client nodes. To
address this, several solutions have been proposed in recent times including
transmitting sparse models and learning dynamic masks iteratively, among
others. However, many of these methods rely on transmitting the model weights
throughout the entire training process as they are based on ad-hoc or random
pruning criteria. In this work, we propose Salient Grads, which simplifies the
process of sparse training by choosing a data aware subnetwork before training,
based on the model-parameter's saliency scores, which is calculated from the
local client data. Moreover only highly sparse gradients are transmitted
between the server and client models during the training process unlike most
methods that rely on sharing the entire dense model in each round. We also
demonstrate the efficacy of our method in a real world federated learning
application and report improvement in wall-clock communication time.
