Revolutionary advances in Internet of Things technologies have paved the way for a
significant increase in computational resources at edge devices that collect condition …

Understanding causal relations is vital in scientific discovery. The process of causal structure
learning involves identifying causal graphs from observational data to understand such …

We study distributed estimation and learning problems in a networked environment where
agents exchange information to estimate unknown statistical properties of random variables …

Background: Data from multiple organizations are crucial for advancing learning health
systems. However, ethical, legal, and social concerns may restrict the use of standard …

Federated learning (FL) has emerged as one of the de-facto privacy-preserving paradigms
that can effectively work with decentralized data sources (eg, hospitals) without acquiring …

Combining multiple predictors obtained from distributed data sources to an accurate meta-
learner is promising to achieve enhanced performance in lots of prediction problems. As the …

In medical research, individual‐level patient data provide invaluable information, but the
patients' right to confidentiality remains of utmost priority. This poses a huge challenge when …

In recent years, diversified measurements reflect the system dynamics from a more
comprehensive perspective in system modeling and analysis, such as scalars, waveform …