Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services
closer to data sources. EC combined with Deep Learning (DL) is a promising technology …

Emerging vehicular applications with strict latency and reliability requirements pose high
computing requirements, and current vehicles' computational resources are not adequate to …

The predominant paradigm for using machine learning models on a device is to train a
model in the cloud and perform inference using the trained model on the device. However …

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

Motivated by the advancing computational capacity of distributed end-user equipment (UE),
as well as the increasing concerns about sharing private data, there has been considerable …

Most studies in cross-device federated learning focus on small models, due to the server-
client communication and on-device computation bottlenecks. In this work, we leverage …

Federated clustering (FC) is an unsupervised learning problem that arises in a number of
practical applications, including personalized recommender and healthcare systems. With …

Communication compression is a common technique in distributed optimization that can
alleviate communication overhead by transmitting compressed gradients and model …

Bilevel optimization has various applications such as hyper-parameter optimization and
meta-learning. Designing theoretically efficient algorithms for bilevel optimization is more …

In large-scale machine learning, recent works have studied the effects of compressing
gradients in stochastic optimization in order to alleviate the communication bottleneck …