Edge Computing (EC) has seen a continuous rise in its popularity as it provides a solution to
the latency and communication issues associated with edge devices transferring data to …

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 …

Federated learning (FL) has attracted widespread attention in the Internet of Things domain
recently. With FL, multiple distributed devices can cooperatively train a global model by …

Federated learning is a popular machine-learning technique that is often preferred due to its
efficiency and data privacy. However, federated-learning systems face a serious threat of …

Federated learning (FL) is a newly emerging distributed learning framework that is
communication-efficient with user privacy guarantee. Wireless end-user devices can …

The applications concerning vehicular networks benefit from the vision of beyond 5G and 6G
technologies such as ultra-dense network topologies, low latency, and high data rates …

Federated learning (FL) has emerged as an ideal privacy-preserving learning technique
which can train a global model in a collaborative way while preserving the private data in the …

Existing federated learning (FL) designs have been shown to exhibit vulnerabilities which
can be exploited by adversaries to compromise data privacy. However, most current works …

To address the backdoor attacks in federated learning due to the inherently distributed and
privacy-preserving peculiarities, we propose RDFL including four components: selecting the …

Federated learning is vulnerable to poisoning attacks in which malicious clients poison the
global model via sending malicious model updates to the server. Existing defenses focus on …