… We analyzed the implementation of federated learning in an edge computing environment
based on its impacts, support, applications, and challenges. The primary studies were …

… We consider a typical edge computing architecture where edge nodes are … federated
learning algorithms, which have general applicability to a wide range of machine learning …

… , resource allocation, incentive learning, security and privacy protection. 3) … edge computing
for several application domains, such as edge data sharing, edge content caching, and edge …

… Inspired by edge computing, we proposed edge federated learning (EdgeFed), which … The
outputs of mobile devices are aggregated in the edge server to improve the learning efficiency …

… deep learning techniques, we propose to integrate the Deep … Learning techniques and
Federated Learning framework with mobile edge systems, for optimizing mobile edge computing, …

… Therefore, edge federated learning is more and more appealing in … to edge computing and
federated learning respectively and … between edge computing and edge federated learning. …

… the typical use cases of federated learning in mobile edge computing, and then investigates …
in federated learning. The resource-efficient techniques for federated learning are broadly …

… In the article, I propose a Verifiable Privacy-preserving Federated Learning scheme that
prevents gradients from leaking during the transfer phase. At the same time, they also present …

… Computer Science and Technology, Soochow University Abstract—Federated learning (FL)
has emerged in edge computing … the edge nodes into K clusters by balanced clustering. The …

… and edge heterogeneity scenarios. To relieve these disadvantages, we propose a novel
semi-asynchronous federated learning (… We consider a federated learning system with a set of …