Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles
(CAV), including perception, planning, and control. However, its reliance on vehicular data …

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

To improve the efficiency of reinforcement learning, we propose a novel asynchronous
federated reinforcement learning framework termed AFedPG, which constructs a global …

Federated learning (FL) is a distributed machine learning (ML) paradigm that enables clients
to collaborate without accessing, infringing upon, or leaking original user data by sharing …

Multimodal federated learning (FL) aims to enrich model training in FL settings where clients
are collecting measurements across multiple modalities. However, key challenges to …

Hierarchical federated learning (HFL) has emerged as the architecture of choice for multi-
level communication networks, mainly because of its data privacy protection and low …