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Adaptive Multi-Model Hierarchical Federated Learning for Robust IoT Intrusion Detection.

Shahid Latif1, Djamel Djenouri1

  • 1School of Computing and Creative Technologies, University of the West of England, Bristol BS16 1QY, UK.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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This study introduces an Adaptive Multi-Model Hierarchical Federated Learning (AMM-HFL) framework to enhance cybersecurity for the Internet of Things (IoT). AMM-HFL improves intrusion detection accuracy, especially with diverse data, by using multiple adaptive models.

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Distributed Systems

Background:

  • The Internet of Things (IoT) presents significant cybersecurity challenges due to its distributed and heterogeneous nature.
  • Traditional intrusion detection systems and federated learning (FL) struggle with non-IID data and adversarial attacks in IoT environments.
  • Centralized aggregation methods in FL are insufficient for robust IoT security.

Purpose of the Study:

  • To propose an Adaptive Multi-Model Hierarchical Federated Learning (AMM-HFL) framework for robust IoT intrusion detection.
  • To address the limitations of traditional FL in handling extreme non-IID data and adversarial conditions.
  • To enhance the accuracy and adaptability of intrusion detection in complex IoT ecosystems.

Main Methods:

Keywords:
adversarial robustnesscybersecurityfederated learningintrusion detection

Related Experiment Videos

  • Developed a three-tier AMM-HFL framework (client, edge, cloud) integrating similarity-aware clustering, multi-model aggregation, and dynamic client-side model selection.
  • Maintained multiple global models for adaptive personalization and better representation of heterogeneous data distributions.
  • Implemented edge-level clustering of model updates to isolate anomalies and cloud-level meta-aggregation for refining diverse models.

    • Main Results:

    • Achieved high detection accuracy on the IDSIoT2024 dataset: up to 97.54% under IID and 97.52% under non-IID conditions.
    • Demonstrated the framework's robustness against extreme non-IID data and adversarial scenarios.
    • Maintained low computational and cryptographic overhead compared to traditional methods.

    Conclusions:

    • The AMM-HFL framework offers a significant advancement in IoT intrusion detection by effectively managing data heterogeneity and adversarial threats.
    • The multi-model and hierarchical approach provides superior adaptability and accuracy compared to single-model FL methods.
    • AMM-HFL presents a scalable and efficient solution for securing the rapidly growing landscape of IoT devices.