… Motivated by the above benefits, we concentrate on the decentralized federated learning (DFL)
[9] and explore the communication efficient training strategy so as to promote …

The sixth-generation network (6G) is expected to achieve a fully connected world, which …
Federated Learning (FL) is an emerging distributed computing paradigm. In Vehicular Edge …

… as local learning and federated learning, and we evaluate the benefits of our FLQ and Δ
FLQ algorithms at reducing the data transmitted among the cloud node and edge servers when …

… In the following work, we propose a methodology that relies on Federated Learning (FL) …
We contribute with a personalized, efficient learning methodology in EC environments that …

… employ centralized learning (CL), which brings significant overhead for data transmission
between the parameter server and vehicular edge devices. Federated learning (FL) framework …

… the phenomenal growth of the data volume generated at the edge network. It has been …
new class of machine learning techniques that shifts computation to the edge network where the …

… 2.4 Problem Formulation We define the adaptive asynchronous federated learning with
resource constraints (AAFL-RC) problem as below. Given a federated learning task, we will …

… Second, the key implementation challenges with existing solutions and several applications
of federated learning in mobile edge networks are discussed. Finally, several open research …

… capabilities of end devices and edge servers to process data closer to where it is … Edge
Intelligence is the privacy preserving machine learning paradigm known as Federated Learning (…

… in the edge server, the new coming vehicle can quickly download the edge network model
of the … A global deep learning network model can be generated in the cloud by gathering the …