Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …

Passenger request prediction is essential for operations planning, control, and management
in ride-hailing platforms. While the demand prediction problem has been studied …

Hierarchical federated learning (HFL) designs introduce intermediate aggregator nodes
between clients and the global federated learning server in order to reduce communication …

As a critical component in federated learning (FL), secure aggregation enables the server to
learn the aggregated model without observing clients' local training gradients. However …

Deep-learning based traffic prediction models require vast amounts of data to learn
embedded spatial and temporal dependencies. The inherent privacy and commercial …

Federated Learning (FL), a crucial advancement in smart city technology, combines real-
time traffic predictions with the potential to enhance urban mobility. This paper suggests a …