Lightweight decentralized learning-based automatic modulation classification method

Abstract

In order to solve the problems in centralized learning, a lightweight decentralized learning-based AMC method was proposed. By the proposed decentralized learning, a global model was trained through local training and model weight sharing, which made full use of the dataset of each communication nodes and avoided the user data leakage. The proposed lightweight network was stacked by a number of different lightweight neural network blocks with a relatively low space complexity and time complexity, and achieved a higher recognition accuracy compared with traditional DL models, which could effectively solve the problems of computing power and storage space limitation of edge devices and high communication overhead in decentralized learning based AMC method. The experimental results show that the classification accuracy of the proposed method is 62.41% based on RadioML.2016.10 A. Compared with centralized learning, the training efficiency is nearly 5 times higher with a slight classification accuracy loss (0.68%). In addition, the experimental results also prove that the deployment of lightweight models can effectively reduce communication overhead in decentralized learning.