Facilitating large-scale, cross-institutional collaboration in biomedical machine learning (ML)
projects requires a trustworthy and resilient federated learning (FL) environment to ensure …

Federated learning (FL) is a distributed machine learning paradigm enabling collaborative
model training while preserving data privacy. In today's landscape, where most data is …

Garbage In Garbage Out is a universally agreed quote by computer scientists from various
domains, including Artificial Intelligence (AI). As data is the fuel for AI, models trained on low …

Federated Learning (FL) is a decentralized machine learning paradigm where models are
trained on distributed devices and are aggregated at a central server. Existing FL …

Cross-silo federated learning offers a promising solution to collaboratively train robust and
generalized AI models without compromising the privacy of local datasets, eg, healthcare …

Clustered federated learning has garnered significant attention as an effective strategy for
enhancing model performance in non-independent and identically distributed (non-IID) data …

The concept of a learning healthcare system (LHS) envisions a self-improving network
where multimodal data from patient care are continuously analyzed to enhance future …

Workflows for machine learning require numerous individuals acting in various capacities.
For instance, users may interact with their devices to generate training data, which is then …

Cross-silo federated learning offers a promising solution to collaboratively train robust and
generalized machine learning models in domains such as healthcare, finance, and scientific …

This paper meticulously examines the privacy challenges stemming from the escalating
volume of data within the new power systems. It thoroughly explores various privacy …