… This paper proposes a federated learning intrusion detection … to wireless edge networks.
FedAGRU uses the computing resources of edge devices and local data sets for model training …

… 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 …

… 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…

… of ML models in general, we focus specifically on DNN model training in this section as a
majority of the papers that we subsequently review study the federated training of DNN models. …

… Abstract—Federated learning (FL) is a distributed learning paradigm that enables a large
number of mobile devices to collaboratively learn a model under the coordination of a central …

… studies a federated learning (FL) system, where multiple FL services co-exist in a wireless
network and share common wireless resources. It fills the void of wireless resource allocation …

… learning (ML) purposes. The easily changed behaviors of edge infrastructure that
software-defined networking (… IoT data at edge servers and gateways, where federated learning …

… learning accuracy level, and thus (ii) between the Federated Learning time and UE energy
consumption… We fill this gap by formulating a Federated Learning over wireless network as an …

… 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 …

… To address such challenges, this paper aims to accelerate FL convergence over wireless
edge by optimizing the multi-hop federated networking performance. In particular, the FL …