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

… 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]…

… the typical use cases of federated learning in mobile edge computing, and then investigates …
in federated learning. The resource-efficient techniques for federated learning are broadly …

… Therefore, edge federated learning is more and more appealing in … to edge computing and
federated learning respectively and … between edge computing and edge federated learning. …

… as federated learning [8]–[10]. We focus on gradient-descent based federated learning
algorithms, which have general applicability to a wide range of machine learning models. The …

… 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 …

… Inspired by edge computing, we proposed edge federated learning (EdgeFed), which separates
the process of updating the local model that is supposed to be completed independently …

… gained attention as a solution to perform learning … applies federated learning to the demand
prediction problem, to accurately forecast the more popular application types in the network. …

… the opportunities and challenges in federated learning. … learn on context aware policies
using a federated learning … challenges associated with federated learning. The challenges can …

… 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 …