The rapid advances in the Internet of Things (IoT) have promoted a revolution in
communication technology and offered various customer services. Artificial intelligence (AI) …

The deployment of the fifth-generation (5G) wireless networks in Internet of Everything (IoE)
applications and future networks (eg, sixth-generation (6G) networks) has raised a number …

Software-defined industrial Internet of things (SD-IIoT) exploits federated learning to process
the sensitive data at edges, while adaptive poisoning attacks threat the security of SD-IIoT …

Contrastive learning pre-trains an image encoder using a large amount of unlabeled data
such that the image encoder can be used as a general-purpose feature extractor for various …

Federated learning (FL) is an emerging machine learning paradigm, in which clients jointly
learn a model with the help of a cloud server. A fundamental challenge of FL is that the …

Federated learning (FL) enables data owners to train a joint global model without sharing
private data. However, it is vulnerable to Byzantine attackers that can launch poisoning …

Although crowdsensing has emerged as a popular information collection paradigm, its
security and privacy vulnerabilities have come to the forefront in recent years. However, one …

Federated recommendation is a prominent use case within federated learning, yet it remains
susceptible to various attacks, from user to server-side vulnerabilities. Poisoning attacks are …

Crowdsourcing has found a wide variety of applications, including spectrum sensing, traffic
monitoring, as well as data annotation for machine learning based data analytics. To …

With the widespread deployment of data-driven services, the demand for data volumes
continues to grow. At present, many applications lack reliable human supervision in the …