… for machine learning (ML) purposes. The easily changed behaviors of edge infrastructure
that … to collate IoT data at edge servers and gateways, where federated learning (FL) can be …

Most conventional Federated Learning (FL) models are using a star network topology where
all users aggregate their local models at a single server (eg, a cloud server). That causes …

… Abstract—We study federated learning (FL) at the wireless edge, where power-limited
devices with local datasets collaboratively train a joint model with the help of a remote parameter …

… devices and to facilitate collaborative machine learning of complex models among distributed
devices, a decentralized ML approach called Federated Learning (FL) is introduced in [21]…

… 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…

… , bandwidth and power) constraints, edge IoT devices may not be able or … edge devices in
a wireless network in training a shared ML model without data exchanges, federated learning (…

… Enabling FL over wireless multi-hop networks can … However, the noisy bandwidth-limited
multi-hop wireless … over wireless edge by optimizing the multi-hop federated networking …

… Abstract—We study federated learning (FL) at the wireless edge, where power-limited devices
with local datasets train a joint model with the help of a remote parameter server (PS). We …

… into federated learning for mitigating stragglers and speeding up the training procedure.
CodedFedL enables coded computing for non-linear federated learning by … at the Wireless Edge …