Federated learning (FL) is a distributed machine learning approach that can achieve the
purpose of collaborative learning from a large amount of data that belong to different parties …

The machine learning performance usually could be improved by training with massive data.
However, requesters can only select a subset of devices with limited training data to execute …

Motivated by the increasingly powerful computing capabilities of end-user equipment, and
by the growing privacy concerns over sharing sensitive raw data, a distributed machine …

Nowadays, there is an ever-increasing deployment of intelligent edge devices, such as
smartphones, wearable devices, and autonomous vehicles. It is enabled by the integration …

Nowadays, researchers are investing their time and devoting their efforts in developing and
motivating the 6G vision and resources that are not available in 5G. Edge computing and …

Industrial Internet of Things (IIoT) is revolutionizing many leading industries such as energy,
agriculture, mining, transportation, and healthcare. IIoT is a major driving force for Industry …

By training a machine learning algorithm across multiple decentralized edge nodes,
federated learning (FL) ensures the privacy of the data generated by the massive Internet-of …

The advent of federated learning (FL) has sparked a new paradigm of parallel and
confidential decentralized machine learning (ML) with the potential of utilizing the …

Big data enables the optimization of complex supply chains through Machine Learning (ML)-
based data analytics. However, data analytics comes with challenges such as the loss of …

Federated Learning (FL) is a collaboratively decentralized privacy-preserving technology to
overcome challenges of data silos and data sensibility. Exactly what research is carrying the …