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March 17, 2026Computer NetworksOpen Access

Hybrid Clustering-Guided Federated Learning for Robust Intrusion Detection in Highly Heterogeneous IoT Environments

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Authors

LGLuis Miguel García-SáezSRSergio Ruiz-VillafrancaJRJosé Roldán-Gómez

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Overview

This work demonstrates enhanced intrusion detection in heterogeneous IoT environments, implying improved security outcomes.

Key Points

  • The aim is to enhance intrusion detection robustness in IoT settings using a novel federated learning approach.
  • Introduced a double-clustering architecture for client-side and server-side coordination.
  • Utilized micro-clustering for local updates to minimize inconsistency.
  • Employed density-based clustering (HDBSCAN) for dynamic client organization.
  • Implemented stability-aware assignments across training rounds.
  • Achieved up to 19.9% increase in F1-score compared to standard federated learning methods.
  • Maintained over 90% peak performance even under severe non-IID conditions.
  • Kept runtime variations within ± 15% across experiments.

Cite This Study

García-Sáez et al. (2026) studied this question.

synapsesocial.com/papers/69b8ef52deb47d591b8c56a7https://doi.org/10.1016/j.comnet.2026.112205
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Application-Oriented Evaluation of Federated Learning for IoT Intrusion Detection Under Non-IID Conditions in Wireless Sensor Networks2026
  2. 2FedMADE: Robust Federated Learning for Intrusion Detection in IoT Networks Using a Dynamic Aggregation Method2024
  3. 3Dynamic Federated Learning Aggregation for Enhanced Intrusion Detection in IoT Attacks2024 · 2 citations
  4. 4Privacy-Preserving Federated Learning-Based Intrusion Detection Technique for Cyber-Physical System2024 · 5 citations
  5. 5Enhancing IoT Security via Federated Learning: A Comprehensive Approach to Intrusion Detection2025 · 5 citations