Federated learning (FL) is a secure distributed machine learning paradigm that addresses
the issue of data silos in building a joint model. Its unique distributed training mode and the …

Federated learning is a privacy-preserving machine learning technique that trains models
across multiple devices holding local data samples without exchanging them. There are …

Given its advantages in low latency, fast response, context-aware services, mobility, and
privacy preservation, edge computing has emerged as the key support for intelligent …

Along with the massive growth of the Internet from the 1990s until now, various innovative
technologies have been created to bring users breathtaking experiences with more virtual …

The research on the sixth-generation (6G) wireless communications for the development of
future mobile communication networks has been officially launched around the world. 6G …

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 …

With the development of communication and networking technologies, the Internet of
Vehicles (IoV) has become the foundation of smart transportation. The development of …

Collaborative sensing leverages the cooperation of a collection of sensors to complete a
large-scale sensing task in Internet of Things (IoT). Although some previous studies have …

Since its inception in 2016, federated learning has evolved into a highly promising decentral-
ized machine learning approach, facilitating collaborative model training across numerous …

The arrival of autonomous vehicles (AVs) promises many great benefits, including increased
safety and reduced energy consumption, pollution, and congestion. However, these engines …