A Review of Federated Learning Applications in Intrusion Detection Systems

Abstract

Intrusion detection systems are evolving into sophisticated systems that perform data analysis while searching for anomalies in their environment. The development of deep learning technologies paved the way to build more complex and effective threat detection models. However, training those models may be computationally infeasible in most Internet of Things devices. Current approaches rely on powerful centralized servers that receive data from all their parties — substantially affecting response times and operational costs due to the huge communication overheads and violating basic privacy constraints. To mitigate these issues, Federated Learning emerged as a promising approach, where different agents collaboratively train a shared model, without exposing training data to others or requiring a compute-intensive centralized infrastructure. This paper focuses on the application of Federated Learning approaches in the field of Intrusion Detection. Both technologies are described in detail and current scientific progress is reviewed and taxonomized. Finally, the paper highlights the limitations present in recent works and proposes some future directions for this technology.