The broad application of artificial intelligence techniques in medicine is currently hindered
by limited dataset availability for algorithm training and validation, due to the absence of …

The rapid developments of the Internet of Things (IoT) and smart mobile devices in recent
years have been dramatically incentivizing the advancement of edge computing. On the one …

Existing foundation models are trained on copyrighted material. Deploying these models
can pose both legal and ethical risks when data creators fail to receive appropriate …

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 distributed empirical risk minimization (ERM) optimization problem with
communication efficiency and privacy requirements, motivated by the federated learning …

We consider training models on private data that are distributed across user devices. To
ensure privacy, we add on-device noise and use secure aggregation so that only the noisy …

Train machine learning models on sensitive user data has raised increasing privacy
concerns in many areas. Federated learning is a popular approach for privacy protection …

Federated Learning (FL) is a paradigm for large-scale distributed learning which faces two
key challenges:(i) training efficiently from highly heterogeneous user data, and (ii) protecting …

This work studies differential privacy in the context of the recently proposed shuffle model.
Unlike in the local model, where the server collecting privatized data from users can track …

With the rapid development of low-cost consumer electronics and pervasive adoption of next
generation wireless communication technologies, a tremendous amount of data has been …