When data privacy is imposed as a necessity, Federated learning (FL) emerges as a
relevant artificial intelligence field for developing machine learning (ML) models in a …

Classical federated learning (FL) enables training machine learning models without sharing
data for privacy preservation, but heterogeneous data characteristic degrades the …

Federated learning (FL) enables large amounts of participants to construct a global learning
model, while storing training data privately at local client devices. A fundamental issue in FL …

Machine learning model training often require data from multiple parties. However, in some
cases, data owners cannot or are not willing to share their data due to legal or privacy …

Federated learning (FL) is a privacy-preserving distributed learning approach that allows
multiple parties to jointly build machine learning models without disclosing sensitive data …

Federated Learning (FL) is an emerging distributed learning paradigm under privacy
constraint. Data heterogeneity is one of the main challenges in FL, which results in slow …

Federated learning (FL) aims to collaboratively train the global model in a distributed
manner by sharing the model parameters from local clients to a central server, thereby …

Federated Learning (FL) involves training a model over a dataset distributed among clients,
with the constraint that each client's dataset is localized and possibly heterogeneous. In FL …

Federated learning (FL) is a joint training pattern that fully utilizes data information whereas
protecting data privacy. A key challenge in FL is statistical heterogeneity, which arises on …