Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Nowadays, the training data and neural network models are getting increasingly large. The
training time of deep learning will become unbearably long on a single machine. To reduce …

We consider a federated learning framework in which a parameter server (PS) trains a
global model by using n clients without actually storing the client data centrally at a cloud …

The number of devices connected to the Internet has already surpassed 1 billion. With the
increasing proliferation of mobile devices, the amount of data collected and transmitted over …

One main challenge in federated learning is the large communication cost of exchanging
weight updates from clients to the server at each round. While prior work has made great …

Federated learning (FL) enables multiple clients to collaboratively train a shared model, with
the help of a parameter server (PS), without disclosing their local datasets. However, due to …

In distributed or federated optimization and learning, communication between the different
computing units is often the bottleneck and gradient compression is widely used to reduce …

We study the mean estimation problem under communication and local differential privacy
constraints. While previous work has proposed order-optimal algorithms for the same …

Sparse tensors appear frequently in federated deep learning, either as a direct artifact of the
deep neural network's gradients, or as a result of an explicit sparsification process. Existing …

Data-parallel distributed training (DDT) has become the de-facto standard for accelerating
the training of most deep learning tasks on massively parallel hardware. In the DDT …