… 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 …

… 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…

… 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 …

… of digital twin edge networks (DITEN), by integrating digital twin with edge computing to …
adopt federated learning to build the models in DITEN. We further present the federated learning …

… 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 …

… edge networks and propose the digital twin edge networks (DITENs) to fill the gap between
physical edge networks … Then, we propose a blockchain-empowered federated learning …

… of collaborative data sharing in vehicular edge networks (VENs) with the deployment of AI-…
sharing scheme with deep Q-network and federated learning to ensure efficient and secure …