Partial federated learning based network intrusion system for mobile devices
Hyoseon Kye, Minhae Kwon · 2022
We propose a partial federated learning based network intrusion system to utilize the limited communication resources of mobile devices. The key to our algorithm is that we share only part of the model to enable efficient communication and strengthen data privacy. The proposed method is evaluated using two network traffic datasets and three performance measures. Our simulation results confirm that the model can reach the performance of centralized learning, although the ratio of a shared layer is reduced up to 25%.