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 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 …

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

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 …

Abstract The Randomized Response (RR) algorithm is a classical technique to improve
robustness in survey aggregation, and has been widely adopted in applications with …

Deep Learning-based computational pathology algorithms have demonstrated profound
ability to excel in a wide array of tasks that range from characterization of well known …

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 …