Privacy-Preserving Federated Transfer Learning for Multi-Class Few-Shot Classification

Abstract Federated learning (FL) is a potential method for training machine learning models that aims to minimize the sharing of data to maximize privacy and performance. However, constructing efficient FL-Network Intrusion Detection Systems(NIDS) requires a substantial quantity of varied training data. Identifying rare attack classes with few shots presents a growing obstacle in this domain, and their detection is a pressing concern. This research proposes a novel FL framework to address the problem by decreasing false alarm rates for unseen attack classes and enhancing the detection of rare classes with a few shots by adaptive personalized layers in FL at the client end. The proposed model employs transfer learning to detect zero-day attacks by gathering tailored client gradients and updating the global model on the server side after observing many rounds of new shots. This strategy aims to disseminate knowledge about rare attack classes to all clients by employing a global model on the server within the federated ecosystem. More precisely, this study has accomplished two significant feats: (i) showcasing the enhancement of identifying rare attack classes and (ii) detecting zero-day attacks in a Network Intrusion Detection System (NIDS) environment by conducting experiments in different scenarios. We thoroughly assessed our proposed approach using the CSE-CICIDS-2018 dataset across several class distribution scenarios. The results exhibit strong performance in identifying and managing novel and rare attack classes compared to current models.