Federated learning faces many security and privacy issues. Among them, poisoning attacks
can significantly impact global models, and malicious attackers can prevent global models …

Federated learning enables multi-participant joint modeling with distributed and localized
training, thus effectively overcoming the problems of data island and privacy protection …

Traditional device fault diagnostic methods in Industrial Internet of Things (IIoT) require
nodes to upload local data to the cloud, which, however, may lead to privacy leakage issues …

Abstract The Internet of Things (IoT) is revolutionizing modern technology by connecting
numerous devices and applications. However, its lack of standardization has led to security …

With the advent of the Internet of things (IoT) era, federated learning plays an important role
in breaking through traditional data barriers and effectively realizing data privacy and …

The rapid development of the Artificial Intelligence of Things (AIoT) opens up a new
perspective for emerging service-based applications and becomes a major driver of diverse …

Machine learning-based detection of false data injection attacks (FDIAs) in smart grids relies
on labeled measurement data for training and testing. The majority of existing detectors are …

As the most popular cryptographic scheme in the post-quantum field, lattices have received
extensive attention and research. Not only do they provide quantum-resistant security, they …

Federated learning allows multiple parties to train models while jointly protecting user
privacy. However, traditional federated learning requires each client to have the same model …

The sixth-generation (6 G) wireless networks are envisioned to build a data-driven digital
world with widespread Artificial Intelligence (AI). Federated learning (FL) is a distributed AI …