… efficient initialization for federated learning, as well … federated learning datasets to highlight
the performance gains of the proposed methods in comparison to the well-known federated …

… this paper, where the mobile edge computing (MEC) technology … We propose to build a
federated learning participant … -quality data to join federated learning. Numerical results derived …

… • First, by formulating an optimization problem that aims to balance the performance and
cost of federated learning, we provide a framework of deploying federated learning in the MEC …

… mobile edge networks. D2D-FedAvg creates a two-tier learning model where D2D learning
… selection, and also D2D exit in the learning process and then form a complete D2D-assisted …

… research directions of federated learning. Finally, we summarize the characteristics of
existing federated learning, and analyze the current practical application of federated learning. …

Federated learning (FL) allows model training from local data collected by edge/mobile …
Our experiments with various datasets on edge devices (eg, Raspberry Pi) show that: 1) we …

… introduce federated learning and present the problem definition in Section III. The proposed
Edge-… Miao, “Federated learning in mobile edge networks: A comprehensive survey,” IEEE …

… Abstract—Federated Learning is a collaborative machine learning framework to train a …
FEDERATED LEARNING SYSTEMS In this section, we first introduce the general learning prob…

… of workers, ie, edge devices, collaboratively learn a shared global … Enabling FL over wireless
multi-hop networks can … wireless edge by optimizing the multi-hop federated networking …

… FL at the edge. In consideration of resource heterogeneity at the edge networks, we then
provide … of concepts derived from network economics, optimization, game theory, and machine …