Federated learning (FL) enables collaboratively training deep learning models on
decentralized data. However, there are three types of heterogeneities in FL setting bringing …

Vast amount of data generated from networks of sensors, wearables, and the Internet of
Things (IoT) devices underscores the need for advanced modeling techniques that leverage …

Privacy protection is an ethical issue with broad concern in artificial intelligence (AI).
Federated learning is a new machine learning paradigm to learn a shared model across …

Federated learning (FL) is a distributed learning paradigm that enables multiple clients to
collaboratively learn a shared global model. Despite the recent progress, it remains …

Personalized federated learning (PFL) aims to train model (s) that can perform well on the
individual edge-devices' data where the edge-devices (clients) are usually IoT devices like …

Personalized decision-making can be implemented in a Federated learning (FL) framework
that can collaboratively train a decision model by extracting knowledge across intelligent …

Federated learning (FL) has been a promising approach in the field of medical imaging in
recent years. A critical problem in FL, specifically in medical scenarios is to have a more …

Federated learning (FL) research has made progress in developing algorithms for
distributed learning of global models, as well as algorithms for local personalization of those …

Federated learning is an emerging learning paradigm where multiple clients collaboratively
train a machine learning model in a privacy-preserving manner. Personalized federated …

There is a growing interest in applying machine learning techniques to healthcare. Recently,
federated machine learning (FL) is gaining popularity since it allows researchers to train …