… 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…

… aspects for enabling federated learning at the network edge. We model … federated learning
via a Stackelberg game to motivate the participation of the devices in the federated learning …

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

… This paper proposes a federated learning intrusion detection model called FedAGRU that
adopts to wireless edge networks. FedAGRU uses the computing resources of edge devices …

… nodes is known as federated learning [8]–[10]. We focus on gradient-descent based federated
learning algorithms, which have general applicability to a wide range of machine learning …

… accuracy edge-based FL, this paper proposes a new FL framework based on cooperative
mobile edge networking called cooperative federated edge learning (… at mobile edge networks. …

… a federated learning system that is more suitable for … learning of discrete nodes in edge
networks and is different from the existing distributed learning system, so that nodes can learn …

… federated learning over wireless edge networks, then discuss the local computation model,
edge … 2.1 Hierarchical Federated Learning Model in Wireless Edge Networks As shown in Fig…

… Federated learning (FL) is a machine learning model that preserves data privacy in the training
… Finally, we present the FL approach to solving (1) in the setting of a mobile edge network, …

… Performance of federated learning in a multi-access edge computing (MEC) network suffers
from slow convergence due to heterogeneity and stochastic fluctuations in compute power …