Model Heterogeneous Federated Learning for Intrusion Detection

Aiming at the problem of model heterogeneity and agent data scarcity, a model heterogeneous federated learning for intrusion detection (MHFL-ID) framework was proposed. First, MHFL-ID groups nodes according to model similarities and differences, that is, models with the same structure were grouped into the same group. Second, the group leader centered isomorphic aggregation method was used to select the group leader according to the projection value of the objective function and guide the optimization direction of the nodes in the group to enhance the modeling capability of the whole group model. Finally, the heterogeneous aggregation method based on knowledge distillation was used between groups to transfer knowledge in heterogeneous models with local average soft label and global soft label without proxy data. Comparative experiments on two datasets, NSL-KDD and UNSW-NB15, show that MHFL-ID framework and the proposed method can effectively solve the problem of heterogeneous model aggregation in federated learning and achieve better results in terms of accuracy.