Scholarlink

The expansion of Internet of Things (IoT) applications in sixth-generation (6G) networks creates new security risks that centralized intrusion detection systems cannot adequately address due to scalability limits, communication overhead, and privacy exposure. We propose a federated learning (FL)-based intrusion detection framework augmented with differential privacy (DP) to protect sensitive data while supporting large-scale IoT deployments. The framework trains models collaboratively across distributed nodes with privacy-preserving updates. Evaluation on benchmark IoT traffic datasets under varied attack scenarios shows that FL with DP maintains high detection accuracy, mitigates information leakage, and scales effectively. These results demonstrate the feasibility of combining FL and DP to deliver robust, privacy-preserving intrusion detection for next-generation IoT networks.

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Privacy-Preserving Federated Learning for IoT Intrusion Detection in 6G Networks

  • 会议论文

  • 发表日期：

    2026年

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  • Jan Ariel Ocampo,
  • Lejla Islami,
  • Mina Alishahi

摘要

The expansion of Internet of Things (IoT) applications in sixth-generation (6G) networks creates new security risks that centralized intrusion detection systems cannot adequately address due to scalability limits, communication overhead, and privacy exposure. We propose a federated learning (FL)-based intrusion detection framework augmented with differential privacy (DP) to protect sensitive data while supporting large-scale IoT deployments. The framework trains models collaboratively across distributed nodes with privacy-preserving updates. Evaluation on benchmark IoT traffic datasets under varied attack scenarios shows that FL with DP maintains high detection accuracy, mitigates information leakage, and scales effectively. These results demonstrate the feasibility of combining FL and DP to deliver robust, privacy-preserving intrusion detection for next-generation IoT networks.