Communication-Efficient Federated Learning for Network Traffic Anomaly Detection

As an emerging machine learning method, Federated Learning is widely used in network anomaly detection scenarios. However, many current federated learning-based network anomaly detection works ignore the communication overhead problem during model training. The transmission of model parameters occupies a large amount of bandwidth, affecting the efficiency of network anomaly detection as well as the communication tasks of other programs. In this study, we propose eFedAD, an efficient federated learning framework for network traffic anomaly detection. Specifically, eFedAD uses singular value decomposition to compress the transmitted parameters and introduces a weighting mechanism to control the compression rate and aggregation proportion. In this way, eFedAD can significantly reduce the number of transmitted parameters and assign higher weights to more valuable clients, enhancing the generalization ability of the global model. Additionally, to address the slow convergence issue due to compressed parameters and minimize the total number of transmitted parameters, eFedAD uses a client selection method that is based on clustering clients’ data feature distributions. The experimental results demonstrate that eFedAD outperforms other compression methods and network anomaly detection approaches, achieving excellent performance.