A methodology for evaluating the effectiveness of intrusion detection system based on federated learning

Abstract

The article investigates the problem of applying federated learning for building intrusion detection systems. Federated learning is a way of organizing distributed learning, in which data are not transferred to a single cloud storage, and the training of models is performed locally, the formation of a common global model is performed based on the parameters of local models. The article proposes a methodology for evaluating the performance of federated trained models, including the evaluation of the training throughput. The training throughput depends on the federated learning settings, the computational resources of the interacting devices and the network bandwidth, and obviously, the use of federated learning can significantly affect the performance of the device itself. This article presents a description of the experimental setup, and the results of a series of experiments that characterize the federated learning process with different settings.