Federated user activity analysis via network traffic and deep neural network in mobile wireless networks

摘要

User activity analysis (UAA) is a promising technology for network management and network security via network traffic. Recently, deep learning (DL) has been applied into network traffic analysis for outstanding performance. These previously proposed network traffic analysis methods generally requires huge amounts of data from network users. In detail, the common methods are to collect these traffic data collected from users into cloud server for processing and analysis, which generally have great performance and are denoted as centralized methods. However, one of the biggest drawbacks of these methods is the risk of data privacy disclosure. Thus, we proposed a federated learning-based UAA method (which is named as FedeUAA) for reducing the risk of data leakage in mobile wireless networks. FedeUAA method has no requirement to upload data to cloud server, while it directly trains the DL models in local devices, and only needs to upload the knowledge (model weight or model gradient) rather than data. Simulation results demonstrated that the FedeUAA method can effectively reduce the risk of data privacy disclosure with slight performance loss. (C) 2021 Elsevier B.V. All rights reserved.