Research on Intelligent Data Classification for Mobile Edge Node Computing Based on Federated Learning
The current methods of using supervised learning to solve network traffic data classification overly rely on high-quality labeled large-scale data sampling,which makes it difficult to directly apply to mobile edge node computing and other issues.There-fore,this study proposes a data classification method combining federated learning with Generate Adversarial Networks(GAN),and introduces self supervised learning to propose an improved model,while verifying its performance.The experimental results show that the values of the three indicators of the improved model in model comparison are 0.90,0.63,and 0.59,which are significantly high-er than the comparison model.Compared with the centralized unsupervised classification model,its actual classification accuracy has improved by more than 20%.In the analysis of different parameters,the index N under parameters a,b,c,and d all began to con-verge at lower training rounds and remained between 0.52 and 0.62 overall.Overall,the improved model proposed in the study has high performance in intelligent data classification and is effective in practical mobile edge node computing data classification.