… that aims to balance the performance and cost of federated learning, we provide a framework
of deploying federated learning in the MEC systems. In particular, we focus on the unre…

… deep learning techniques, we propose to integrate the Deep … Learning techniques and
Federated Learning framework with mobile edge systems, for optimizing mobile edge computing, …

… is the intensive resources of mobile … of federated learning in mobile edge computing, and
then investigates the state-of-the-art resource optimization approaches in federated learning. …

… Local data storing and processing with global coordination is made possible by the emerging
technology of mobile edge computing (MEC) [4], [5], where edge nodes, such as sensors, …

… have contributed to the emergence of the new mobile edge computing (MEC) paradigm [[4]–…
of edge-nodes with the remote cloud in order to give rise to a computing system able to …

… 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. …

… An autonomous self-learning distributed system [15] utilized a federated learning approach
… , there are two protocols proposed in [16] to verify data integrity for mobile edge computing. …

… Meanwhile, Federated learning (FL) has emerged as a … layer federated learning protocol
called HybridFL is designed for the MEC architecture. HybridFL adopts two levels (the edge …

… learning in edge computing systems. Therefore, it can be widely used in many scenarios
where privacy protection and resource utilization are critical. In this section, we will discuss a …

… It is a technology that enables the training of ML models on mobile edge networks. Therefore,
the communication costs, security, privacy, and legalization issues could be alleviated by …