DRL-based Federated Learning Node Selection Algorithm for Mobile Edge Networks

Massive amounts of data have given a huge boost to artificial intelligence for communication networks, for instance, intelligent inspection, power IoT management, but they have also brought problems. The original data generated at the edge of the mobile communication network and imported into the core network not only takes up a lot of bandwidth resources, but also poses a great challenge to the fast and reliable transmission and computing. Traditional cloud-based machine learning methods require data to be centralized in cloud servers or data centers. However, in edge networks, due to limited network resources, direct transmission of centrally learned data will lead to unacceptable communication delays, resulting in low system efficiency, and may lead to serious privacy problems. In order to solve these problems, federal learning technology is attracting people's attention. This paper first analyzes the factors that affect the efficiency of federated learning system, establishes a federated learning system model, then uses DDPG to design and implement a node selection algorithm, the goal is to reduce the federated learning time to the maximum and improve the learning accuracy. Finally, under the condition of different node quality, the simulation experiment verifies that the algorithm can shorten 40% of the model training stability time, thus proving the effectiveness and feasibility of the proposed algorithm, indicating that the federated learning system can effectively select nodes in this way.