Recent developments in the Internet of Things (IoT) and various communication
technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into …

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by
supporting and contributing to the evidence-based practice of medicine, personalizing …

In healthcare, machine learning (ML) shows significant potential to augment patient care,
improve population health, and streamline healthcare workflows. Realizing its full potential …

Trustworthy artificial intelligence (TAI) has proven invaluable in curbing potential negative
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …

Abstract We introduce Dataset Grouper, a library to create large-scale group-structured (eg,
federated) datasets, enabling federated learning simulation at the scale of foundation …

Over the past few years, Federated Learning (FL) has become an emerging machine
learning technique to tackle data privacy challenges through collaborative training. In the …

Conformal prediction is emerging as a popular paradigm for providing rigorous uncertainty
quantification in machine learning since it can be easily applied as a post-processing step to …

Federated learning facilitates the collaborative learning of a global model across multiple
distributed medical institutions without centralizing data. Nevertheless the expensive cost of …

Partially class-disjoint data (PCDD), a common yet under-explored data formation where
each client contributes a part of classes (instead of all classes) of samples, severely …

Federated learning has been extensively explored in privacy-preserving medical image
analysis. However, the domain shift widely existed in real-world scenarios still greatly limits …