… We analyzed the implementation of federatedlearning in an edgecomputing environment based on its impacts, support, applications, and challenges. The primary studies were …
… We consider a typical edgecomputing architecture where edge nodes are … federated learning algorithms, which have general applicability to a wide range of machine learning …
Y Ye, S Li, F Liu, Y Tang, W Hu - IEEE Access, 2020 - ieeexplore.ieee.org
… Inspired by edgecomputing, we proposed edgefederatedlearning (EdgeFed), which … The outputs of mobile devices are aggregated in the edge server to improve the learning efficiency …
… , resource allocation, incentive learning, security and privacy protection. 3) … edgecomputing for several application domains, such as edge data sharing, edge content caching, and edge …
R Yu, P Li - IEEE Network, 2021 - ieeexplore.ieee.org
… the typical use cases of federatedlearning in mobile edgecomputing, and then investigates … in federatedlearning. The resource-efficient techniques for federatedlearning are broadly …
… In the article, I propose a Verifiable Privacy-preserving FederatedLearning scheme that prevents gradients from leaking during the transfer phase. At the same time, they also present …
… deep learning techniques, we propose to integrate the Deep … Learning techniques and FederatedLearning framework with mobile edge systems, for optimizing mobile edgecomputing, …
D Ye, R Yu, M Pan, Z Han - IEEE Access, 2020 - ieeexplore.ieee.org
… Section II presents related work of federatedlearning in edge computing and distributed networks. Section III describes a general framework of federatedlearning in VEC. Section IV …
C Feng, Z Zhao, Y Wang, TQS Quek… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
… • First, by formulating an optimization problem that aims to balance the performance and cost of federatedlearning, we provide a framework of deploying federatedlearning in the MEC …