Federated learning is a distributed machine learning approach that enables multiple
participants to collaboratively train a model without sharing their data, thus preserving …

With the intelligence and automation of industrial Internet of Things, a new collaborative
Cloud-Fog Automation paradigm has emerged. The emergence of federated learning (FL) …

In the emerging Industrial Internet of Things (IIoT) applications, Federated Learning (FL)
enables model training without the need to transmit raw data directly. Nevertheless …

Machine learning models can support decision-making in mobile terminals (MTs)
deployments, but their training generally requires massive datasets and abundant …

Federated meta-learning (ML) is a promising optimization framework for the intelligent
Internet of Things (IoT). However, the generalization ability of existing federated ML is limited …

The exponential growth of the Internet of Things (IoT) has boosted connectivity across
various sectors, such as Industry 4.0 and smart cities. However, this expansion has also …

Integrating machine learning (ML) into blockchain consensus mechanisms enhances
efficiency, scalability, and resilience. This study introduces the PoA 2 algorithm, an ML …

Combining the Internet of Things (IoT) and federated learning (FL) is a trend. In addition to
privacy risks, a long-term operating IoT always faces a hierarchical environment …

The number of interacting devices is increasing every day, and with this constant innovation,
serious security challenges arise. The concept of the Internet of Things is being actively …

Federated Learning is a collaborative machine learning framework that enables model
training using private client data while preserving privacy. Differential Privacy enhances this …