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 …

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 …

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 …

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 …

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 …

Federated learning allows for cooperative training among distributed clients by sharing their
locally learned model parameters, such as weights or gradients. However, as model size …

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

Federated learning (FL) enables clients to collaboratively train a model, while keeping their
local training data decentralized. However, high communication costs, data heterogeneity …

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 …