A review of Federated Learning in Intrusion Detection Systems for IoT

TL;DR

This paper reviews how Federated Learning enhances Intrusion Detection Systems in IoT by enabling collaborative, privacy-preserving model training without centralized data collection, addressing computational and communication challenges.

Contribution

It provides a comprehensive review and categorization of Federated Learning applications in IoT intrusion detection, highlighting current progress, limitations, and future research directions.

Findings

Federated Learning reduces privacy risks in IoT intrusion detection.

Current methods face limitations in communication efficiency and model accuracy.

Future work should address scalability and robustness challenges.

Abstract

Intrusion detection systems are evolving into intelligent systems that perform data analysis searching for anomalies in their environment. The development of deep learning technologies opened the door 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 -- violating basic privacy constraints and substantially affecting response times and operational costs due to the huge communication overheads. To mitigate these issues, Federated Learning emerged as a promising approach where different agents collaboratively train a shared model, neither exposing training data to others nor 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 categorized. Finally, the paper highlights the limitations present in recent works and presents some future directions for this technology.