… 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 …

Edge Intelligence based on federated learning (FL) can be considered to be a promising
paradigm for many emerging, strict latency Internet of Things (IoT) applications. Furthermore, a …

… efficiency in federated learning Since conventional FedL in large-scale networks can incur
… investigated avenues to reduce the resource burden of edge devices in FedL. In particular, a …

… Aiming to achieve communication efficiency in FL over a relay-assisted edge network, we
propose a novel synchronization scheme named PSP. By exploring alternative transmission …

… To build a federated learning system, we use the PySyft [24] framework, built on top of PyTorch
… To deploy an automated federated learning mechanism in our MUDcompliant network we …

… As for the communication time over mobile edge networks, we consider that UEs access
the BS through a channel partitioning scheme, such as orthogonal frequency division multiple …

… Inspired by edge computing, we proposed edge federated learning (EdgeFed), which … The
outputs of mobile devices are aggregated in the edge server to improve the learning efficiency …

… For the sake of trust, security, and efficiency of distributed AI in the mobile edge network,
this article introduces a novel blockchain-FL fusion framework. The contributions of this article …

… The learning algorithm is run within edge devices, and only … as Federated Edge Learning
(FEEL) throughout this paper as our work focuses on deploying FL in wireless edge networks. …

… six years in the fields of edge computing and federated learning, as illustrated in … federated
learning in 2016, the number of publications related to federated learning in an edge network …