Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

Decentralized federated learning (DFL) uses peer-to-peer communication to avoid the
single point of failure problem in federated learning and has been considered an attractive …

Federated recommender systems have been crucially enhanced through data sharing and
continuous model updates, attributed to the pervasive connectivity and distributed …

In traditional machine learning, it is trivial to conduct model evaluation since all data
samples are managed centrally by a server. However, model evaluation becomes a …

The proliferation of intelligence services brings data breaches and privacy infringement
concerns. To preserve data privacy when training machine learning models, the federated …

Federated learning (FL) is a distributed machine learning paradigm in which clients
collaboratively train models in a privacy-preserving manner. While centralized FL (CFL) …

Recently, federated learning (FL) has received intensive research because of its ability in
preserving data privacy for scattered clients to collaboratively train machine learning …

Decentralized model training for on-road vehicles offers the potential to harness huge
amounts of data at low costs. However, existing approaches usually depend on the …

Federated Learning (FL) aims to train a shared model using data and computation power on
distributed agents coordinated by a central server. Decentralized FL (DFL) utilizes local …

Edge intelligence and federated learning (FL), as key enablers of 6G, is a promising solution
for networked Autonomous Driving (NAD). However, traditional federated learning is a …