For healthcare datasets, it is often impossible to combine data samples from multiple sites
due to ethical, privacy, or logistical concerns. Federated learning allows for the utilization of …

In recent years, decentralized machine learning has emerged as a significant advancement
in biomedical applications, offering robust solutions for data privacy, security, and …

Survival analysis is a fundamental tool in medicine, modeling the time until an event of
interest occurs in a population. However, in real-world applications, survival data are often …

Survival analysis is a subfield of statistics concerned with modeling the occurrence time of a
particular event of interest for a population. Survival analysis found widespread applications …

A distributed learning technique named Federated Learning (FL) is utilized by mobile
devices, clinical research labs, and hospitals for secure healthcare data sharing. FL has …

Communication networks are crucial components of the underlying digital infrastructure in
any smart city setup. The increasing usage of computer networks brings additional cyber …

In recent years, rapid technological advancements have propelled blockchain and artificial
intelligence (AI) into prominent roles within the digital industry, each having unique …

Trustworthy Artificial Intelligence solutions are essential in today's data-driven applications,
prioritizing principles such as robustness, safety, transparency, explainability, and privacy …

Currently, the baking of cakes using an electric oven is based on cooking duration. Usually,
colors can be used to determine the levels of cooking food. However, many cakes have …

Federated learning is extensively applied in collaborative data scenarios involving multiple
data owners. While the majority of state-of-the-art federated learning algorithms are currently …