S Liu, J Yu, X Deng, S Wan - IEEE Transactions on Intelligent …, 2021 - ieeexplore.ieee.org
The sixth-generation network (6G) is expected to achieve a fully connected world, which … FederatedLearning (FL) is an emerging distributed computing paradigm. In Vehicular Edge …
… as local learning and federatedlearning, 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 FederatedLearning (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. Federatedlearning (FL) framework …
… the phenomenal growth of the data volume generated at the edgenetwork. It has been … new class of machine learning techniques that shifts computation to the edgenetwork where the …
J Liu, H Xu, L Wang, Y Xu, C Qian… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
… 2.4 Problem Formulation We define the adaptive asynchronous federatedlearning with resource constraints (AAFL-RC) problem as below. Given a federatedlearning task, we will …
… Second, the key implementation challenges with existing solutions and several applications of federatedlearning in mobile edgenetworks 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 FederatedLearning (…
H Xiao, J Zhao, Q Pei, J Feng, L Liu… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
… in the edge server, the new coming vehicle can quickly download the edgenetwork model of the … A global deep learningnetwork model can be generated in the cloud by gathering the …