The Internet of Things (IoT) is penetrating many aspects of our daily life with the proliferation
of artificial intelligence applications. Federated learning (FL) has emerged as a promising …

In recent years, privacy and security concerns in machine learning have promoted trusted
federated learning to the forefront of research. Differential privacy has emerged as the de …

With the rapid development of artificial intelligence (AI), combining machine learning (ML)
and edge computing powered by big data has become a growing trend. However, under …

The applications of Vision-Language Models (VLMs) in the field of Autonomous Driving (AD)
have attracted widespread attention due to their outstanding performance and the ability to …

Consumer electronic devices often involve processing and analyzing a large amount of user
personal data. Nevertheless, owing to apprehensions regarding privacy and security, users …

Differentially-Private Federated Learning (DPFL) is an emerging privacy-preserving
distributed machine learning paradigm that allows for the automatic recognition of human …

The proliferation of distributed sensitive data in recent years in network edge devices
motivates the introduction of edge computing which moves machine learning (ML) …

Vertical Federated Learning (VFL) has emerged as a collaborative training paradigm that
allows participants with different features of the same group of users to accomplish …

Vertical Federated Learning (VFL) is widely utilized in real-world applications to enable
collaborative learning while protecting data privacy and safety. However, previous works …

Social recommendation refers to recommendation technology taking social relations as
additional input to improve merchandise sales and user satisfaction. It has been widely used …