… edge network optimization. However, in a large-scale and complex mobile edge network, …
Furthermore, we present the applications of FL for mobile edge network optimization. Finally…

… training round. In this paper, we analyze how to design adaptive FL in mobile edge networks
… -cost sampling-based algorithm to learn the convergence related unknown parameters. We …

… latency but low-accuracy edge-based FL, this paper proposes a new FL framework based
on cooperative mobile edge networking called cooperative federated edge learning (CFEL) to …

… intensive resources of mobile clients in … of federated learning in mobile edge computing,
and then investigates the state-of-the-art resource optimization approaches in federated learning…

… of multiple federated learning services at the multi-access edge … learning services at each
mobile device for the local training … among mobile devices for exchanging learning information …

… In this article, we addressed worker selection issues to ensure reliable federated learning
in mobile networks. A reputation-based scheme was designed to select reliable and trusted …

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

… Finally, we present the FL approach to solving (1) in the setting of a mobile edge network, …
policy for FL in the context of mobile edge networks. By adopting a metric termed AoU, our …

… gained attention as a solution to perform learning … applies federated learning to the demand
prediction problem, to accurately forecast the more popular application types in the network. …

… training process, especially for large scale FL systems with straggling nodes. This article
proposes a novel edge network … process at the server, thereby significantly reducing the training …