FL Intelligent Wireless Network Optimization System
Yaowen Sun · 2023
Currently, wireless network research focuses on homogeneous networks, i.e., research based on a single node and connection. However, in practical applications, most systems are complex heterogeneous FL (Federated Learning) information networks containing multiple nodes and multiple labeling classes. In this paper, we address this problem and study the optimization of heterogeneous information networks based on FL models with artificial intelligence and optimization methods in wireless networks. In this paper, we propose an innovative "FL Intelligent Wireless Network Optimization System", which fills the research gap in the field of heterogeneous network optimization, and can well solve the problem of heterogeneous information networks with many different semantic information features, and at the same time, it is difficult to solve the problem of the intersection of multiple semantics effectively. Through experimental validation, our system achieves significant results in multi-labeling scenarios, effectively improving prediction accuracy and network performance. Through this study, we fill the research gap in the field of heterogeneous network optimization and provide strong support for the further development of the wireless network field.