As a privacy-preserving paradigm for training machine learning (ML) models, federated
learning (FL) has received tremendous attention from both industry and academia. In a …

Federated learning (FL) has gained increasing attention due to its ability to collaboratively
train while protecting client data privacy. However, vanilla FL cannot adapt to client …

Deep learning-based Autonomous Driving (AD) models often exhibit poor generalization
due to data heterogeneity in an ever domain-shifting environment. While Federated …

The heterogeneous connections in metaverse environments pose vulnerabilities to cyber-
attacks. To prevent and mitigate malicious network activities in a distributed metaverse …

This paper surveys recent research on federated learning-based resource allocation for next-
generation networks in order to identify research gaps and potential future directions. We …

Vehicular edge intelligence (VEI) is a promising paradigm for enabling future intelligent
transportation systems by accommodating artificial intelligence (AI) at the vehicular edge …

With the promise of federated learning (FL) to allow for geographically-distributed and highly
personalized services, the efficient exchange of model updates between clients and servers …

Federated Learning is a training framework that enables multiple participants to
collaboratively train a shared model while preserving data privacy. The heterogeneity of …

Abstract Internet of Things (IoT) gained momentum these last few years pushed by the
emergence of fast and reliable communication networks such as 5G and beyond. IoT …

In conventional federated learning, each device is restricted to train a network model of a
same structure. This greatly hinders the application of federated learning in edge devices …