… optimization algorithms for FL based on edge computing are proposed to implement the
three-layer architecture of ‘client-edge… client-edge’, and the global aggregation is between ‘edge-…

… Federated Learning (FL), a distributed deep learning paradigm [8]. It allows vehicles to use
local data to train their local deep learning … So this flexible learning method is suitable for …

… is enabled by FL because it allows DL models to be trained collaboratively to optimize … We
analyzed the implementation of federated learning in an edge computing environment based …

… and training efficiency of federated learning cannot be guaranteed. To solve this problem, the
optimization design of federated learning in … First, an optimization problem is formulated to …

… Combination of DRL training and FL in this paper is studied in the edge computing-supported
IoT system. After performing experiments on a use case, viz., computation task offloading, …

… federated learning, the data is collected at the edge directly and stored persistently at edge
nodes, thus the data distribution at different edge … this paper, optimization of synchronization …

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

… 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 systems. …

… federated learning is how to efficiently utilize the limited computation and communication
resources for realizing the optimal learning … GGS) framework for federated learning in edge com…

… Zhang, “Incentive mechanism for reliable federated learning: A joint optimization approach
to combining reputation and contract theory,” IEEE Internet of Things Journal, 2019, to be …