mlsanityData to 7 Oct 2026

A review of Federated Learning in Intrusion Detection Systems for IoT

arXiv 2204.12443 ↗First posted 26 Apr 2022cs.CR cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 52 of its 146 references that are in the map. First posted on arXiv 26 Apr 2022.

Builds on

52 of 146 references.

References · 52

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38 more
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50 most cited of 52 All 52 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 0; 2024: 0; 2025: 0; 2026: 1.

4 papers.

  1. 2022Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networkscited by 5
  2. 2022CPS Attack Detection under Limited Local Information in Cyber Security: A Multi-node Multi-class Classification Ensemble Approachcited by 2
  3. 2022GowFed -- A novel Federated Network Intrusion Detection Systemcited by 2
  4. 2026Collaborative Zone-Adaptive Zero-Day Intrusion Detection for IoBTcited by 0